From cb789c3ef1245aa985266f00f5172256727b6fbe Mon Sep 17 00:00:00 2001
From: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
Date: Thu, 14 May 2026 11:19:28 +0400
Subject: [PATCH] feat: Working vastai single-node deployment for LLM inference

---
 .dockerignore                                 |    2 +
 Cargo.toml                                    |    2 +-
 examples/single-gpu-inference/Cargo.lock      | 4949 +++++++++++++++++
 examples/single-gpu-inference/Cargo.toml      |   29 +
 examples/single-gpu-inference/Dockerfile      |   27 +
 .../INTEGRATION_TEST_PLAN.md                  |   78 +
 examples/single-gpu-inference/Makefile        |   13 +
 examples/single-gpu-inference/SPEC.md         |  243 +
 examples/single-gpu-inference/echo_worker.py  |   16 +
 .../single-gpu-inference/src/bin/gpu_node.rs  |  310 ++
 .../src/bin/single_gpu_inference.rs           |  573 ++
 .../src/inference_actor.rs                    |  220 +
 .../src/iroh_transport.rs                     |  161 +
 examples/single-gpu-inference/src/lib.rs      |    4 +
 examples/single-gpu-inference/src/messages.rs |   67 +
 examples/single-gpu-inference/src/vastai.rs   |  292 +
 .../single-gpu-inference/tests/t_actor.rs     |  215 +
 .../single-gpu-inference/tests/t_binary.rs    |  164 +
 .../single-gpu-inference/tests/t_cluster.rs   |  270 +
 .../single-gpu-inference/tests/t_codec.rs     |  153 +
 .../tests/t_integration.rs                    |  415 ++
 .../single-gpu-inference/tests/t_vastai.rs    |  235 +
 .../single-gpu-inference/tests/test_worker.py |  154 +
 .../single-gpu-inference/tinygrad_worker.py   |  159 +
 24 files changed, 8750 insertions(+), 1 deletion(-)
 create mode 100644 examples/single-gpu-inference/Cargo.lock
 create mode 100644 examples/single-gpu-inference/Cargo.toml
 create mode 100644 examples/single-gpu-inference/Dockerfile
 create mode 100644 examples/single-gpu-inference/INTEGRATION_TEST_PLAN.md
 create mode 100644 examples/single-gpu-inference/Makefile
 create mode 100644 examples/single-gpu-inference/SPEC.md
 create mode 100644 examples/single-gpu-inference/echo_worker.py
 create mode 100644 examples/single-gpu-inference/src/bin/gpu_node.rs
 create mode 100644 examples/single-gpu-inference/src/bin/single_gpu_inference.rs
 create mode 100644 examples/single-gpu-inference/src/inference_actor.rs
 create mode 100644 examples/single-gpu-inference/src/iroh_transport.rs
 create mode 100644 examples/single-gpu-inference/src/lib.rs
 create mode 100644 examples/single-gpu-inference/src/messages.rs
 create mode 100644 examples/single-gpu-inference/src/vastai.rs
 create mode 100644 examples/single-gpu-inference/tests/t_actor.rs
 create mode 100644 examples/single-gpu-inference/tests/t_binary.rs
 create mode 100644 examples/single-gpu-inference/tests/t_cluster.rs
 create mode 100644 examples/single-gpu-inference/tests/t_codec.rs
 create mode 100644 examples/single-gpu-inference/tests/t_integration.rs
 create mode 100644 examples/single-gpu-inference/tests/t_vastai.rs
 create mode 100644 examples/single-gpu-inference/tests/test_worker.py
 create mode 100644 examples/single-gpu-inference/tinygrad_worker.py

diff --git a/.dockerignore b/.dockerignore
index 17eb168..311f784 100644
--- a/.dockerignore
+++ b/.dockerignore
@@ -1,2 +1,4 @@
 *
 !target/x86_64-unknown-linux-musl/release/swactor
+!examples/single-gpu-inference/target/release/gpu-node
+!examples/single-gpu-inference/tinygrad_worker.py
diff --git a/Cargo.toml b/Cargo.toml
index 0fc2ee4..b3ba13a 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -14,7 +14,7 @@ members = [
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     "xtask",
 ]
-exclude = ["crates/bindings/wasm-crypto"]
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diff --git a/examples/single-gpu-inference/Cargo.lock b/examples/single-gpu-inference/Cargo.lock
new file mode 100644
index 0000000..a082424
--- /dev/null
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+ "zerocopy-derive",
+]
+
+[[package]]
+name = "zerocopy-derive"
+version = "0.8.39"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "4122cd3169e94605190e77839c9a40d40ed048d305bfdc146e7df40ab0f3e517"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn",
+]
+
+[[package]]
+name = "zerofrom"
+version = "0.1.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "50cc42e0333e05660c3587f3bf9d0478688e15d870fab3346451ce7f8c9fbea5"
+dependencies = [
+ "zerofrom-derive",
+]
+
+[[package]]
+name = "zerofrom-derive"
+version = "0.1.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "d71e5d6e06ab090c67b5e44993ec16b72dcbaabc526db883a360057678b48502"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn",
+ "synstructure",
+]
+
+[[package]]
+name = "zeroize"
+version = "1.8.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "b97154e67e32c85465826e8bcc1c59429aaaf107c1e4a9e53c8d8ccd5eff88d0"
+dependencies = [
+ "zeroize_derive",
+]
+
+[[package]]
+name = "zeroize_derive"
+version = "1.4.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "85a5b4158499876c763cb03bc4e49185d3cccbabb15b33c627f7884f43db852e"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn",
+]
+
+[[package]]
+name = "zerotrie"
+version = "0.2.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "2a59c17a5562d507e4b54960e8569ebee33bee890c70aa3fe7b97e85a9fd7851"
+dependencies = [
+ "displaydoc",
+ "yoke",
+ "zerofrom",
+]
+
+[[package]]
+name = "zerovec"
+version = "0.11.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6c28719294829477f525be0186d13efa9a3c602f7ec202ca9e353d310fb9a002"
+dependencies = [
+ "yoke",
+ "zerofrom",
+ "zerovec-derive",
+]
+
+[[package]]
+name = "zerovec-derive"
+version = "0.11.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "eadce39539ca5cb3985590102671f2567e659fca9666581ad3411d59207951f3"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn",
+]
+
+[[package]]
+name = "zmij"
+version = "1.0.21"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "b8848ee67ecc8aedbaf3e4122217aff892639231befc6a1b58d29fff4c2cabaa"
diff --git a/examples/single-gpu-inference/Cargo.toml b/examples/single-gpu-inference/Cargo.toml
new file mode 100644
index 0000000..aaa3df3
--- /dev/null
+++ b/examples/single-gpu-inference/Cargo.toml
@@ -0,0 +1,29 @@
+[workspace]
+
+[package]
+name = "single-gpu-inference"
+version = "0.1.0"
+edition = "2024"
+publish = false
+
+[dependencies]
+swactor = { path = "../..", features = ["transport", "serde", "std"] }
+swactor-process = { path = "../../crates/process" }
+serde = { version = "1", features = ["derive"] }
+serde_json = "1"
+reqwest = { version = "0.12", features = ["json"] }
+tokio = { version = "1", features = ["full"] }
+distribution = { path = "../../crates/distribution", features = ["iroh"] }
+iroh = "0.96"
+urlencoding = "2"
+
+[[bin]]
+name = "gpu-node"
+path = "src/bin/gpu_node.rs"
+
+[[bin]]
+name = "single-gpu-inference"
+path = "src/bin/single_gpu_inference.rs"
+
+[dev-dependencies]
+wiremock = "0.6"
diff --git a/examples/single-gpu-inference/Dockerfile b/examples/single-gpu-inference/Dockerfile
new file mode 100644
index 0000000..2119dd2
--- /dev/null
+++ b/examples/single-gpu-inference/Dockerfile
@@ -0,0 +1,27 @@
+FROM nvidia/cuda:12.6.3-devel-ubuntu24.04
+
+# Install Python 3, pip, and CUDA runtime compiler (tinygrad compiles kernels via NVRTC)
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+        python3 \
+        python3-venv \
+        python3-pip \
+        ca-certificates && \
+    rm -rf /var/lib/apt/lists/*
+
+# Install tinygrad and numpy
+RUN python3 -m pip install --no-cache-dir --break-system-packages \
+    tinygrad==0.12.0 \
+    numpy
+
+# Copy the gpu-node binary and tinygrad worker
+# Build context should be the workspace root:
+#   docker build -f examples/single-gpu-inference/Dockerfile -t <tag> .
+COPY examples/single-gpu-inference/target/release/gpu-node /usr/local/bin/gpu-node
+COPY examples/single-gpu-inference/tinygrad_worker.py /usr/local/share/tinygrad_worker.py
+
+# Enable CUDA backend for tinygrad
+ENV CUDA=1
+ENV WORKER_SCRIPT=/usr/local/share/tinygrad_worker.py
+
+CMD ["gpu-node"]
diff --git a/examples/single-gpu-inference/INTEGRATION_TEST_PLAN.md b/examples/single-gpu-inference/INTEGRATION_TEST_PLAN.md
new file mode 100644
index 0000000..46859d8
--- /dev/null
+++ b/examples/single-gpu-inference/INTEGRATION_TEST_PLAN.md
@@ -0,0 +1,78 @@
+# Plan: Local Mock Integration Test
+
+## Context
+
+The smoke-test crate has 5 component test groups (T-codec, T-worker, T-vastai, T-actor, T-cluster) that each test a piece of the distributed inference pipeline in isolation. What's missing is a single test that wires them together: two swactor nodes on localhost, one running the real `InferenceActor` (with `echo_worker.py`), communicating over iroh/QUIC. This proves the full local chain before spending money on vast.ai.
+
+## Design Problem
+
+`InferenceActor::Incoming` is `InferenceActorMsg` (a union of `Request` and `Process` variants). But the network codec delivers raw `InferenceRequest`. When `rt.deliver_raw()` delivers a deserialized `InferenceRequest` to the `InferenceActor`, the downcast to `InferenceActorMsg` fails silently.
+
+**Solution:** Add a `RequestBridge` actor — same pattern as the existing `ProcessBridge`. It receives `InferenceRequest` from the network, wraps it as `InferenceActorMsg::Request(req)`, and forwards to the `InferenceActor`. ~10 lines.
+
+## Changes
+
+### 1. Add `RequestBridge` to `examples/single-gpu-inference/src/inference_actor.rs`
+
+A public actor struct placed after the existing `ProcessBridge` (~line 55). Fields: `target: ActorAddress`. Implements `ActorInterface` with `Incoming = InferenceRequest`, wraps and forwards to target as `InferenceActorMsg::Request`.
+
+### 2. Export it from `examples/single-gpu-inference/src/lib.rs`
+
+Already exports `pub mod inference_actor` — `RequestBridge` just needs to be `pub`.
+
+### 3. Create `examples/single-gpu-inference/tests/t_integration.rs`
+
+One test function: `distributed_inference_through_echo_worker`.
+
+**Setup (reuse patterns from t_cluster.rs and t_actor.rs):**
+- Copy iroh helpers: `make_driver`, `make_converged_pair`, `IrohActorTransport`, `encode_wire`/`decode_wire`, `drain_actor_messages` (with reduced internal sleep for localhost — 100ms instead of 500ms)
+- Copy process helpers: `echo_worker_spec`, `is_process_alive`
+
+**Test flow:**
+1. Converge two iroh drivers via `make_converged_pair()`
+2. Create `rt_a` (local) and `rt_b` (remote) runtimes
+3. On `rt_b`: spawn `InferenceActor` (with `echo_worker_spec()`) + `RequestBridge` pointing at it
+4. On `rt_a`: create `response_inbox` for `InferenceResponse`
+5. Build `IrohActorTransport` in each direction, wire transport routers:
+   - `rt_a`: `bridge_addr → transport_a_to_b`
+   - `rt_b`: `inbox_addr → transport_b_to_a`
+6. Install codec registries and transport routers (`&mut self` — must happen after all spawns/inbox creation)
+7. Tick `rt_b` in a polling loop until `InferenceActorStatus::WorkerReady` (echo_worker.py started)
+8. `rt_a.send_to(bridge_addr, InferenceRequest { prompt: "Hello from node A", reply_to: inbox_addr, ... })`
+9. Pump loop (10s timeout): `drain_actor_messages` on both drivers → tick both runtimes → check `response_inbox`
+10. Assert response text contains `"Hello from node A"` (echo worker reflects prompt)
+11. Cleanup: stop inference actor, tick until echo_worker.py pid is dead, shutdown both drivers
+
+**Message path through the system:**
+```
+rt_a.send_to(bridge_addr, InferenceRequest)
+  → transport router → IrohActorTransport (QUIC to node B)
+  → drain_actor_messages → rt_b.deliver_raw(bridge_addr, InferenceRequest)
+  → RequestBridge.handle() → ctx.send(inference_addr, InferenceActorMsg::Request(req))
+  → InferenceActor.handle() → writes JSON to echo_worker.py stdin
+  → echo_worker.py → writes JSON to stdout
+  → ProcessActor → ProcessNotification::Output → ProcessBridge → InferenceActor
+  → InferenceActor.process_output_line() → ctx.send(reply_to, InferenceResponse)
+  → transport router → IrohActorTransport (QUIC to node A)
+  → drain_actor_messages → rt_a.deliver_raw(inbox_addr, InferenceResponse)
+  → response_inbox.try_recv() ✓
+```
+
+## Files Modified
+
+| File | Change |
+|---|---|
+| `examples/single-gpu-inference/src/inference_actor.rs` | Add `pub struct RequestBridge` (~10 lines) |
+| `examples/single-gpu-inference/tests/t_integration.rs` | New file — one integration test (~200 lines) |
+
+## Verification
+
+```bash
+# Run just the new test
+cargo test --manifest-path examples/single-gpu-inference/Cargo.toml t_integration
+
+# Confirm existing tests still pass
+cargo test --manifest-path examples/single-gpu-inference/Cargo.toml
+```
+
+Expected: all 22 tests pass (21 existing + 1 new). Test runtime ~10-15s (dominated by iroh drain sleeps and echo_worker.py process I/O).
diff --git a/examples/single-gpu-inference/Makefile b/examples/single-gpu-inference/Makefile
new file mode 100644
index 0000000..17a8b5f
--- /dev/null
+++ b/examples/single-gpu-inference/Makefile
@@ -0,0 +1,13 @@
+.PHONY: venv clean-venv
+
+VENV_DIR := .venv
+PYTHON := $(VENV_DIR)/bin/python
+
+venv: $(VENV_DIR)/bin/python
+
+$(VENV_DIR)/bin/python:
+	uv venv $(VENV_DIR) --python python3.11
+	uv pip install --python $(VENV_DIR)/bin/python "tinygrad==0.12.0" numpy
+
+clean-venv:
+	rm -rf $(VENV_DIR)
diff --git a/examples/single-gpu-inference/SPEC.md b/examples/single-gpu-inference/SPEC.md
new file mode 100644
index 0000000..e2ba804
--- /dev/null
+++ b/examples/single-gpu-inference/SPEC.md
@@ -0,0 +1,243 @@
+# Smoke Test Specification: Rent a GPU, Run Inference, Say Hello
+
+## 1. Wanted Behavior
+
+`smoke-run --vastai --api-key <key>` runs on the local machine. It:
+1. Finds the cheapest available GPU on vast.ai and rents it.
+2. Starts a local swactor node with iroh transport.
+3. The rented instance boots our pre-built Docker image containing `gpu-node` + tinygrad.
+4. The remote `gpu-node` reads `SEED_ADDR` from env and joins the local cluster via iroh/QUIC.
+5. The local node discovers the remote `InferenceActor` via SWIM name resolution (`"inference"`).
+6. The local node sends an `InferenceRequest` to the remote actor.
+7. The remote actor runs tinygrad inference, replies with `InferenceResponse`.
+8. Asserts the response is non-empty text and prints it.
+9. Destroys the vast.ai instance.
+10. Exits 0.
+
+If anything fails, the script destroys the instance (if one was created) and exits 1.
+
+Total wall-clock budget: 10 minutes.
+
+```
+local machine (smoke-run)               vast.ai GPU (gpu-node)
+    |                                       |
+    |  1. search offers (REST)              |
+    |  2. create instance (REST)            |
+    |     SEED_ADDR=<local node id>         |
+    | ------------------------------------> |
+    |                                       | [pulling docker image]
+    |  3. poll vast.ai status every 10s     | [gpu-node starting]
+    | ------------------------------------> | [tinygrad loading model into VRAM]
+    |       "loading"                       |
+    | <------------------------------------ |
+    |       "running"                       |
+    | <------------------------------------ |
+    |                                       |
+    |  4. SWIM cluster join (iroh/QUIC)     |
+    | <--------------------------------->>> |
+    |       [cluster converged]             |
+    |       [name "inference" resolved]     |
+    |                                       |
+    |  5. InferenceRequest (swactor msg)    |
+    |     "Say hello"                       |
+    | ------------------------------------> |
+    |                                       | [tinygrad forward pass]
+    |       InferenceResponse               |
+    |       "Hello! How can I help you?"    |
+    | <------------------------------------ |
+    |                                       |
+    |  6. assert len(response) > 0          |
+    |  7. DELETE instance (vast.ai REST)    |
+    | ------------------------------------> |
+    |  8. exit 0                            |
+```
+
+---
+
+## 2. What Already Exists
+
+The entire pipeline is implemented and tested locally. The remaining work is deploying it to vast.ai for real.
+
+### Implemented components
+
+| Component | File(s) | Status |
+|---|---|---|
+| Message types + codec | `src/messages.rs` | Done, tested (T-codec: 7 tests) |
+| InferenceActor + ProcessBridge | `src/inference_actor.rs` | Done, tested (T-actor: 4 tests) |
+| RequestBridge (network→actor type bridge) | `src/inference_actor.rs` | Done |
+| Iroh actor transport (shared) | `src/iroh_transport.rs` | Done, used by tests + binaries |
+| vast.ai REST client | `src/vastai.rs` | Done, tested (T-vastai: 7 tests) |
+| tinygrad worker (real GGUF model) | `tinygrad_worker.py` | Done, tested (T-worker: 5 test classes) |
+| Cluster transport tests | `tests/t_cluster.rs` | Done (3 tests) |
+| In-process integration test (echo) | `tests/t_integration.rs` | Done, passes |
+| In-process integration test (tinygrad) | `tests/t_integration.rs` | Done, passes (`#[ignore]`, needs .venv) |
+| `gpu-node` binary | `src/bin/gpu_node.rs` | Done, compiles |
+| `smoke-run` binary (localhost + vastai) | `src/bin/smoke_run.rs` | Done, compiles |
+| Binary e2e test (echo) | `tests/t_binary.rs` | Done, passes |
+| Binary e2e test (tinygrad) | `tests/t_binary.rs` | Done, passes (`#[ignore]`, needs .venv) |
+| Dockerfile | `Dockerfile` | Done |
+
+### Test commands
+
+```bash
+# Fast tests (no GPU, no downloads, no vast.ai) — 22 tests
+cargo test --manifest-path examples/single-gpu-inference/Cargo.toml
+
+# Slow tests (downloads ~1GB GGUF model, runs tinygrad on CPU)
+cargo test --manifest-path examples/single-gpu-inference/Cargo.toml -- --ignored
+
+# Binary e2e on localhost (spawns gpu-node + smoke-run as child processes)
+cargo test --manifest-path examples/single-gpu-inference/Cargo.toml binary_e2e_echo_worker
+```
+
+---
+
+## 3. What Remains — Deployment to vast.ai
+
+### 3.1 Fix relay mode for WAN
+
+Both binaries currently use `RelayMode::Disabled`, which works on localhost but not over WAN. The remote `gpu-node` behind a vast.ai NAT cannot reach the local node without iroh relay servers.
+
+**Changes needed:**
+- `gpu-node`: change `RelayMode::Disabled` to `RelayMode::Default` so iroh uses its public relay infrastructure for NAT traversal.
+- `smoke-run` (vastai path): same — use `RelayMode::Default`.
+- The localhost path (`smoke-run --seed`) can keep `RelayMode::Disabled`.
+
+### 3.2 Fix worker script path in Dockerfile
+
+The `Dockerfile` copies `tinygrad_worker.py` to `/usr/local/share/tinygrad_worker.py`, but `gpu-node` defaults `WORKER_SCRIPT` to `./tinygrad_worker.py`.
+
+**Fix**: either change the Dockerfile `COPY` destination to `/app/tinygrad_worker.py` and set `WORKDIR /app`, or set `ENV WORKER_SCRIPT=/usr/local/share/tinygrad_worker.py` in the Dockerfile.
+
+### 3.3 Add `pid` to tinygrad worker ready signal
+
+`tinygrad_worker.py` emits `{"status": "ready"}` but `echo_worker.py` emits `{"status": "ready", "pid": <pid>}`. The `InferenceActor` parses the `pid` field for `WorkerReady { pid }` status reporting. Without it, `worker_pid` is `None` — not fatal, but cleanup assertions in tests rely on it.
+
+**Fix**: change the ready signal to `_write({"status": "ready", "pid": os.getpid()})`.
+
+### 3.4 Build and push the Docker image
+
+```bash
+# Cross-compile gpu-node for linux/amd64 (if not already on linux/amd64)
+cargo build --release --bin gpu-node --manifest-path examples/single-gpu-inference/Cargo.toml
+
+# Build image
+docker build -t <your-registry>/swactor-gpu:latest -f examples/single-gpu-inference/Dockerfile .
+
+# Push to registry (vast.ai pulls from here)
+docker push <your-registry>/swactor-gpu:latest
+```
+
+The image name in `vastai.rs::create_instance` is hardcoded to `"swactor-gpu:latest"`. Update this to match whatever registry you push to, or make it a parameter.
+
+### 3.5 Run it for real
+
+```bash
+VAST_API_KEY=<key> cargo run --manifest-path examples/single-gpu-inference/Cargo.toml --bin single-gpu-inference -- \
+    --vastai --api-key <key> --gpu RTX_4090
+```
+
+---
+
+## 4. Architecture
+
+### Message flow (same for localhost and vast.ai)
+
+```
+smoke-run                                    gpu-node
+─────────                                    ────────
+rt.send_to(bridge_addr, InferenceRequest)
+  → codec encodes → TransportRouter
+  → IrohActorTransport (QUIC to gpu-node)
+                                             drain_and_collect_reply_addrs()
+                                               → decode_wire → codecs.receive()
+                                               → rt.deliver_raw(bridge_addr, InferenceRequest)
+                                               → extract reply_to, add return transport route
+                                             RequestBridge.handle()
+                                               → ctx.send(inference_addr, InferenceActorMsg::Request)
+                                             InferenceActor.handle()
+                                               → JSON to tinygrad_worker.py stdin
+                                             tinygrad_worker.py
+                                               → Transformer forward pass
+                                               → JSON response to stdout
+                                             InferenceActor.process_output_line()
+                                               → ctx.send(reply_to, InferenceResponse)
+                                               → TransportRouter → IrohActorTransport (QUIC back)
+drain_actor_messages()
+  → decode_wire → codecs.receive()
+  → rt.deliver_raw(inbox_addr, InferenceResponse)
+response_inbox.try_recv() ✓
+```
+
+### Name discovery
+
+The `gpu-node` registers its `RequestBridge` under the name `"inference"` via `DistributedNode::register_name`. This propagates through SWIM gossip piggyback. The `smoke-run` orchestrator calls `driver.node().resolve_name("inference")` to discover the bridge's `ActorAddress` without needing to know it ahead of time.
+
+### Dynamic return routing
+
+The `gpu-node`'s drain loop inspects incoming `InferenceRequest` payloads to extract the `reply_to` address. It then dynamically registers a transport route for that address pointing back to the only alive SWIM member (the orchestrator). This is necessary because the `gpu-node` doesn't know the orchestrator's inbox address at startup.
+
+---
+
+## 5. tinygrad Worker
+
+`tinygrad_worker.py` — managed by swactor's process crate via stdin/stdout JSON.
+
+**Model**: `llama3.2:1b` from tinygrad's built-in GGUF catalog (~1GB download, fits in any modern GPU's VRAM). Loaded via `tinygrad.apps.llm.Transformer.from_gguf()`.
+
+**Protocol**:
+```
+← stdout: {"status": "ready", "pid": 12345}
+→ stdin:  {"prompt": "Say hello", "max_tokens": 64, "temperature": 0.7}
+← stdout: {"response": "Hello! How can I help you today?"}
+→ stdin:  {invalid json}
+← stdout: {"error": "invalid JSON: ..."}
+```
+
+**Modes**:
+- Default: downloads and loads real GGUF model, runs real inference.
+- `--stub`: canned responses, no tinygrad import (for fast protocol tests).
+- `--model <name>`: override model (e.g., `qwen3:0.6b` for smaller download).
+
+**Environment**:
+- `CUDA=1` → tinygrad uses CUDA backend (GPU).
+- No env var → tinygrad auto-detects (CUDA if available, else CPU).
+- `PYTHON=1` → forces tinygrad's pure-Python CPU backend (no clang needed).
+
+---
+
+## 6. Error Modes
+
+| Failure | Detection | Response |
+|---------|-----------|----------|
+| No GPU offers available | vast.ai returns empty list | Print error, exit 1. No cleanup needed. |
+| Instance creation rejected | API returns non-success | Print error, exit 1. No cleanup needed. |
+| Instance never reaches `running` | 60 polls exhausted or terminal status | Destroy instance, exit 1. |
+| Cluster never converges | SWIM timeout (2 min) | Destroy instance, exit 1. |
+| Name `"inference"` never resolves | Timeout (1 min) | Destroy instance, exit 1. |
+| tinygrad worker never reports ready | Process timeout (10 min) | Destroy instance, exit 1. |
+| Empty/malformed response | Assertion on response text | Destroy instance, exit 1. |
+| Destroy fails | Catch around cleanup | Print warning, exit 1. |
+
+Every path that allocates an instance also destroys it.
+
+---
+
+## 7. Environment Requirements
+
+- Rust toolchain
+- Python 3.10+ with tinygrad 0.12.0 + numpy (in `.venv/`)
+- Docker (for building the GPU node image)
+- `VAST_API_KEY` (vast.ai deployment only — not needed for local tests)
+- Network access to iroh relay servers (vast.ai deployment only)
+
+---
+
+## 8. What This Proves
+
+1. swactor nodes on rented GPUs can join a cluster with a local node over WAN via iroh/QUIC.
+2. swactor messages traverse the WAN transparently — no HTTP layer needed for node-to-node communication.
+3. tinygrad can load and run a LLaMA model on rented hardware via its CUDA backend.
+4. The process crate manages the Python child process lifecycle cleanly.
+5. SWIM gossip propagates actor name registrations, enabling dynamic service discovery.
+6. The full lifecycle (rent → cluster → discover → infer → teardown) is fully automated.
diff --git a/examples/single-gpu-inference/echo_worker.py b/examples/single-gpu-inference/echo_worker.py
new file mode 100644
index 0000000..aa0e7f3
--- /dev/null
+++ b/examples/single-gpu-inference/echo_worker.py
@@ -0,0 +1,16 @@
+#!/usr/bin/env python3
+"""Stub worker for testing. Same JSON protocol as tinygrad_worker.py."""
+import json, os, sys
+
+print(json.dumps({"status": "ready", "pid": os.getpid()}), flush=True)
+for line in sys.stdin:
+    line = line.strip()
+    if not line:
+        continue
+    try:
+        req = json.loads(line)
+        if req.get("prompt") == "__crash__":
+            os._exit(1)
+        print(json.dumps({"response": f"echo: {req['prompt']}"}), flush=True)
+    except Exception as e:
+        print(json.dumps({"error": str(e)}), flush=True)
diff --git a/examples/single-gpu-inference/src/bin/gpu_node.rs b/examples/single-gpu-inference/src/bin/gpu_node.rs
new file mode 100644
index 0000000..f995528
--- /dev/null
+++ b/examples/single-gpu-inference/src/bin/gpu_node.rs
@@ -0,0 +1,310 @@
+//! gpu-node — GPU inference node for distributed tinygrad inference.
+//!
+//! Runs inside a Docker container (or locally for testing). Joins an existing
+//! cluster via `SEED_ADDR`, spawns an `InferenceActor` backed by
+//! `tinygrad_worker.py`, and registers the bridge under the name `"inference"`
+//! so the orchestrator can discover it.
+//!
+//! Environment variables:
+//! - `SEED_ADDR` (required): hex-encoded NodeId of the seed node to join
+//! - `SEED_DIRECT` (optional): comma-separated `ip:port` direct addresses for the seed
+//! - `WORKER_CMD` (optional): path to Python interpreter (default: `python3`)
+//! - `WORKER_SCRIPT` (optional): path to tinygrad_worker.py (default: `./tinygrad_worker.py`)
+//! - `PYTHON` (optional): set to `1` for CPU fallback backend (no GPU/clang)
+
+use std::collections::HashMap;
+use std::net::SocketAddr;
+use std::sync::Arc;
+use std::time::{Duration, Instant};
+
+use distribution::iroh_driver::{IrohDriver, IrohDriverConfig};
+use distribution::node::DistributedNodeConfig;
+use distribution::registry::RegistryConfig;
+use distribution::swim::probe::SwimConfig;
+use iroh::RelayMode;
+
+use swactor::actor::ActorAddress;
+use swactor::runtime::{Runtime, RuntimeConfig};
+use swactor::transport::{CodecRegistry, TransportRouter};
+
+use single_gpu_inference::inference_actor::{InferenceActor, InferenceActorStatus, RequestBridge};
+use single_gpu_inference::iroh_transport::{decode_wire, IrohActorTransport, ACTOR_ALPN};
+use single_gpu_inference::messages::{inference_codec_registry, InferenceRequest};
+use swactor_process::{ProcessMode, ProcessSpec};
+
+fn parse_hex_node_id(s: &str) -> [u8; 32] {
+    assert!(
+        s.len() == 64,
+        "SEED_ADDR must be 64 hex characters (32 bytes)"
+    );
+    let mut bytes = [0u8; 32];
+    for i in 0..32 {
+        bytes[i] = u8::from_str_radix(&s[i * 2..i * 2 + 2], 16)
+            .unwrap_or_else(|_| panic!("invalid hex at position {}", i * 2));
+    }
+    bytes
+}
+
+fn node_config() -> DistributedNodeConfig {
+    DistributedNodeConfig {
+        swim: SwimConfig {
+            probe_interval: 10,
+            probe_timeout: 15,
+            indirect_probes: 2,
+            suspicion_timeout: 60,
+            dead_reprobe_interval: 100,
+            ..SwimConfig::default()
+        },
+        cache_capacity: 100,
+        republish_interval: 50,
+        registry: RegistryConfig::default(),
+        metadata_lambda: 3,
+    }
+}
+
+fn worker_spec() -> ProcessSpec {
+    let cmd = std::env::var("WORKER_CMD").unwrap_or_else(|_| "python3".into());
+    let script =
+        std::env::var("WORKER_SCRIPT").unwrap_or_else(|_| "./tinygrad_worker.py".into());
+
+    let mut env = HashMap::new();
+    if let Ok(val) = std::env::var("PYTHON") {
+        env.insert("PYTHON".into(), val);
+    }
+
+    ProcessSpec {
+        command: cmd,
+        args: vec![script],
+        env,
+        working_dir: None,
+        mode: ProcessMode::Automated,
+        initial_pty_size: None,
+        kill_timeout: Some(Duration::from_secs(5)),
+        stdin_buffer_limit: None,
+    }
+}
+
+/// Drain incoming actor messages from iroh into the swactor runtime.
+///
+/// Like `drain_actor_messages` from the shared module, but also inspects
+/// `InferenceRequest` payloads to extract `reply_to` addresses. Returns
+/// the set of reply-to addresses found so the caller can dynamically
+/// register transport routes for the response path.
+fn drain_and_collect_reply_addrs(
+    driver: &IrohDriver,
+    codecs: &CodecRegistry,
+    rt: &Runtime,
+    drain_sleep: Duration,
+) -> Vec<ActorAddress> {
+    let conns = driver.drain_other_connections();
+    if conns.is_empty() {
+        return vec![];
+    }
+    let handle = driver.tokio_handle();
+    let (tx, rx) = std::sync::mpsc::channel::<Vec<u8>>();
+
+    for (_node_id, conn) in conns {
+        let tx = tx.clone();
+        handle.spawn(async move {
+            loop {
+                match tokio::time::timeout(Duration::from_secs(1), conn.accept_uni()).await {
+                    Ok(Ok(mut recv)) => {
+                        if let Ok(data) = recv.read_to_end(256 * 1024).await {
+                            let _ = tx.send(data);
+                        }
+                    }
+                    _ => break,
+                }
+            }
+        });
+    }
+    drop(tx);
+    std::thread::sleep(drain_sleep);
+
+    let mut reply_addrs = Vec::new();
+    for data in rx.try_iter() {
+        if let Some(envelope) = decode_wire(&data) {
+            // Inspect InferenceRequest payloads for reply_to addresses
+            if envelope.type_tag == "smoke::InferenceRequest" {
+                if let Ok(req) = serde_json::from_slice::<InferenceRequest>(&envelope.payload) {
+                    reply_addrs.push(req.reply_to);
+                }
+            }
+            if let Ok((addr, msg)) = codecs.receive(envelope) {
+                let _ = rt.deliver_raw(addr, msg);
+            }
+        }
+    }
+    reply_addrs
+}
+
+fn main() {
+    // Create iroh driver
+    let mut driver = IrohDriver::new(IrohDriverConfig {
+        secret_key: None,
+        relay_mode: RelayMode::Default,
+        node: node_config(),
+        peer_auth: None,
+        additional_alpns: vec![ACTOR_ALPN.to_vec()],
+    })
+    .expect("failed to create iroh driver");
+
+    // Print node address info so tests/orchestrators can discover us
+    let my_id = driver.node_id();
+    let my_hex: String = my_id.0.iter().map(|b| format!("{:02x}", b)).collect();
+    let direct_addrs: Vec<String> = driver
+        .direct_addresses()
+        .iter()
+        .map(|sa| sa.to_string())
+        .collect();
+    let direct_str = direct_addrs.join(",");
+    eprintln!("gpu-node started (node_id: {my_hex})");
+    eprintln!("GPU_NODE_ADDR {my_hex} {direct_str}");
+
+    // Optionally join a seed node (required for remote deployments,
+    // optional for localhost testing where the orchestrator joins us)
+    if let Ok(seed_hex_raw) = std::env::var("SEED_ADDR") {
+        let seed_hex = seed_hex_raw.trim().to_string();
+        let seed_bytes = parse_hex_node_id(&seed_hex);
+        let seed_key =
+            iroh::PublicKey::from_bytes(&seed_bytes).expect("invalid seed public key");
+
+        let mut seed_addr = iroh::EndpointAddr::from(seed_key);
+        // Add relay URL so we can find the seed over the internet
+        if let Ok(relay) = std::env::var("SEED_RELAY") {
+            let relay = relay.trim().to_string();
+            if let Ok(relay_url) = relay.parse::<iroh::RelayUrl>() {
+                eprintln!("using seed relay: {relay}");
+                seed_addr = seed_addr.with_relay_url(relay_url);
+            }
+        }
+        if let Ok(direct) = std::env::var("SEED_DIRECT") {
+            for part in direct.split(',') {
+                if let Ok(sa) = part.trim().parse::<SocketAddr>() {
+                    seed_addr = seed_addr.with_ip_addr(sa);
+                }
+            }
+        }
+
+        eprintln!("joining seed: {seed_hex}");
+        driver.join(&[seed_addr]);
+    } else {
+        eprintln!("no SEED_ADDR set — listening for incoming connections");
+    }
+
+    // Create actor runtime
+    let mut rt = Runtime::new(RuntimeConfig::default());
+    let codecs = Arc::new(inference_codec_registry());
+
+    // Spawn InferenceActor + RequestBridge
+    let status_inbox = rt.new_inbox::<InferenceActorStatus>().unwrap();
+    let sender = rt.create_sender();
+    let actor =
+        InferenceActor::new(worker_spec(), sender).with_status_addr(*status_inbox.addr());
+    let inference_addr = rt.spawn(actor).unwrap();
+
+    let bridge = RequestBridge { target: inference_addr };
+    let bridge_addr = rt.spawn(bridge).unwrap();
+
+    // Register "inference" name in the cluster
+    driver
+        .node_mut()
+        .register_name("inference".into(), bridge_addr);
+    eprintln!("registered name 'inference' -> bridge {:?}", bridge_addr);
+
+    // Install codecs on the runtime
+    rt.set_codec_registry(codecs.clone());
+
+    // Transport router — routes are added dynamically as requests arrive
+    let router = Arc::new(TransportRouter::new());
+    rt.set_transport_router(router.clone());
+
+    // Wait for worker to be ready (model download + load can take minutes)
+    let start = Instant::now();
+    let mut worker_ready = false;
+    while start.elapsed() < Duration::from_secs(600) {
+        rt.tick();
+        if let Some(status) = status_inbox.try_recv() {
+            match status {
+                InferenceActorStatus::WorkerReady { pid } => {
+                    eprintln!("worker ready (pid: {:?})", pid);
+                    worker_ready = true;
+                    break;
+                }
+                InferenceActorStatus::ProcessStarted => {
+                    eprintln!("worker process started");
+                }
+                InferenceActorStatus::ProcessExited { status } => {
+                    eprintln!("worker exited during startup: {:?}", status);
+                    std::process::exit(1);
+                }
+            }
+        }
+        driver.recv();
+        driver.tick();
+        std::thread::sleep(Duration::from_millis(50));
+    }
+    if !worker_ready {
+        eprintln!("worker did not become ready within 600s");
+        std::process::exit(1);
+    }
+
+    // Main loop
+    eprintln!("entering main loop");
+    loop {
+        driver.recv();
+        driver.tick();
+
+        // Drain incoming actor messages and collect reply_to addresses
+        let reply_addrs =
+            drain_and_collect_reply_addrs(&driver, &codecs, &rt, Duration::from_millis(50));
+
+        // Dynamically register transport routes for reply_to addresses.
+        // These addresses live on the remote orchestrator node — we need a
+        // transport route so InferenceActor can send InferenceResponse back.
+        for reply_addr in reply_addrs {
+            // Find the peer to route back to. With a single orchestrator peer,
+            // we route all reply addresses to the only alive member.
+            let snap = driver.snapshot();
+            for member in &snap.members {
+                if member.state == "alive" && member.node_id.len() == 64 {
+                    let peer_bytes = parse_hex_node_id(&member.node_id);
+                    if let Ok(peer_key) = iroh::PublicKey::from_bytes(&peer_bytes) {
+                        let peer_addr = iroh::EndpointAddr::from(peer_key);
+                        let transport = Arc::new(IrohActorTransport::new(
+                            driver.endpoint().clone(),
+                            peer_addr,
+                            driver.tokio_handle(),
+                        ));
+                        router.add_route(reply_addr, transport);
+                        eprintln!("added return route for {:?}", reply_addr);
+                    }
+                }
+            }
+        }
+
+        rt.tick();
+
+        // Check worker health — log but don't exit, so SWIM stays alive
+        // for diagnostics when the worker crashes
+        if let Some(status) = status_inbox.try_recv() {
+            match status {
+                InferenceActorStatus::ProcessExited { status } => {
+                    eprintln!("worker process exited: {:?}", status);
+                    eprintln!("keeping main loop alive for diagnostics");
+                }
+                other => {
+                    eprintln!("worker status: {:?}", other);
+                }
+            }
+        }
+
+        std::thread::sleep(Duration::from_millis(10));
+    }
+
+    // Cleanup
+    let _ = rt.stop_actor(inference_addr);
+    rt.tick();
+    driver.shutdown();
+    eprintln!("gpu-node shut down");
+}
diff --git a/examples/single-gpu-inference/src/bin/single_gpu_inference.rs b/examples/single-gpu-inference/src/bin/single_gpu_inference.rs
new file mode 100644
index 0000000..b563f1a
--- /dev/null
+++ b/examples/single-gpu-inference/src/bin/single_gpu_inference.rs
@@ -0,0 +1,573 @@
+//! single-gpu-inference — Local-side orchestrator for distributed tinygrad inference.
+//!
+//! Starts a local iroh node, waits for the remote gpu-node to join and
+//! register its `"inference"` name, then sends an `InferenceRequest` through
+//! the full distributed pipeline and prints the response.
+//!
+//! Usage:
+//!   # Localhost mode (default): expects gpu-node already running with SEED_ADDR
+//!   single-gpu-inference --seed <hex-node-id> [--seed-direct ip:port,...]
+//!
+//!   # vast.ai mode: rents a GPU, deploys the Docker image, runs the test
+//!   single-gpu-inference --vastai --api-key <key> [--gpu RTX_4090]
+//!
+//! Environment variables:
+//!   PYTHON=1  — passed through to worker (CPU fallback, no GPU/clang)
+
+use std::net::SocketAddr;
+use std::sync::Arc;
+use std::time::{Duration, Instant};
+
+use distribution::iroh_driver::{IrohDriver, IrohDriverConfig};
+use distribution::node::DistributedNodeConfig;
+use distribution::registry::RegistryConfig;
+use distribution::swim::probe::SwimConfig;
+use iroh::{PublicKey, RelayMode};
+
+use swactor::runtime::{Runtime, RuntimeConfig};
+use swactor::transport::TransportRouter;
+
+use single_gpu_inference::iroh_transport::{drain_actor_messages, IrohActorTransport, ACTOR_ALPN};
+use single_gpu_inference::messages::{inference_codec_registry, InferenceRequest, InferenceResponse};
+
+fn parse_hex_node_id(s: &str) -> [u8; 32] {
+    assert!(
+        s.len() == 64,
+        "node id must be 64 hex characters (32 bytes)"
+    );
+    let mut bytes = [0u8; 32];
+    for i in 0..32 {
+        bytes[i] = u8::from_str_radix(&s[i * 2..i * 2 + 2], 16)
+            .unwrap_or_else(|_| panic!("invalid hex at position {}", i * 2));
+    }
+    bytes
+}
+
+fn node_config() -> DistributedNodeConfig {
+    DistributedNodeConfig {
+        swim: SwimConfig {
+            probe_interval: 10,
+            probe_timeout: 15,
+            indirect_probes: 2,
+            suspicion_timeout: 60,
+            dead_reprobe_interval: 100,
+            ..SwimConfig::default()
+        },
+        cache_capacity: 100,
+        republish_interval: 50,
+        registry: RegistryConfig::default(),
+        metadata_lambda: 3,
+    }
+}
+
+fn print_usage() {
+    eprintln!("Usage:");
+    eprintln!("  single-gpu-inference --seed <hex-node-id> [--seed-direct ip:port,...]");
+    eprintln!("  single-gpu-inference --vastai --api-key <key> [--gpu RTX_4090] [--image ghcr.io/user/swactor-gpu:latest]");
+}
+
+fn main() {
+    let args: Vec<String> = std::env::args().collect();
+
+    let mut seed_hex: Option<String> = None;
+    let mut seed_direct: Vec<SocketAddr> = Vec::new();
+    let mut vastai = false;
+    let mut api_key: Option<String> = None;
+    let mut gpu_name = "RTX 4090".to_string();
+    let mut image = "swactor-gpu:latest".to_string();
+
+    let mut i = 1;
+    while i < args.len() {
+        match args[i].as_str() {
+            "--seed" => {
+                i += 1;
+                if i >= args.len() {
+                    eprintln!("--seed requires a value");
+                    std::process::exit(1);
+                }
+                seed_hex = Some(args[i].clone());
+            }
+            "--seed-direct" => {
+                i += 1;
+                if i >= args.len() {
+                    eprintln!("--seed-direct requires a value");
+                    std::process::exit(1);
+                }
+                for part in args[i].split(',') {
+                    if let Ok(sa) = part.trim().parse::<SocketAddr>() {
+                        seed_direct.push(sa);
+                    }
+                }
+            }
+            "--vastai" => {
+                vastai = true;
+            }
+            "--api-key" => {
+                i += 1;
+                if i >= args.len() {
+                    eprintln!("--api-key requires a value");
+                    std::process::exit(1);
+                }
+                api_key = Some(args[i].trim().to_string());
+            }
+            "--gpu" => {
+                i += 1;
+                if i >= args.len() {
+                    eprintln!("--gpu requires a value");
+                    std::process::exit(1);
+                }
+                gpu_name = args[i].clone();
+            }
+            "--image" => {
+                i += 1;
+                if i >= args.len() {
+                    eprintln!("--image requires a value");
+                    std::process::exit(1);
+                }
+                image = args[i].clone();
+            }
+            "--help" | "-h" => {
+                print_usage();
+                return;
+            }
+            other => {
+                eprintln!("unknown argument: {other}");
+                print_usage();
+                std::process::exit(1);
+            }
+        }
+        i += 1;
+    }
+
+    if vastai {
+        run_vastai(api_key.expect("--api-key required with --vastai"), &gpu_name, &image);
+    } else {
+        let seed = seed_hex.expect("--seed required in localhost mode");
+        run_localhost(&seed, &seed_direct);
+    }
+}
+
+fn run_localhost(seed_hex: &str, seed_direct: &[SocketAddr]) {
+    let seed_bytes = parse_hex_node_id(seed_hex);
+    let seed_key =
+        iroh::PublicKey::from_bytes(&seed_bytes).expect("invalid seed public key");
+
+    // Build seed endpoint address
+    let mut seed_addr = iroh::EndpointAddr::from(seed_key);
+    for &sa in seed_direct {
+        seed_addr = seed_addr.with_ip_addr(sa);
+    }
+
+    // Create local iroh driver
+    let mut driver = IrohDriver::new(IrohDriverConfig {
+        secret_key: None,
+        relay_mode: RelayMode::Disabled,
+        node: node_config(),
+        peer_auth: None,
+        additional_alpns: vec![ACTOR_ALPN.to_vec()],
+    })
+    .expect("failed to create iroh driver");
+
+    let my_id = driver.node_id();
+    let my_hex: String = my_id.0.iter().map(|b| format!("{:02x}", b)).collect();
+    eprintln!("single-gpu-inference started (node id: {my_hex})");
+    eprintln!("joining seed: {seed_hex}");
+
+    driver.join(&[seed_addr.clone()]);
+
+    // Wait for SWIM convergence — the remote node must appear as alive
+    eprintln!("waiting for cluster convergence...");
+    let start = Instant::now();
+    let mut converged = false;
+    while start.elapsed() < Duration::from_secs(30) {
+        driver.recv();
+        driver.tick();
+
+        let peer_key = PublicKey::from_bytes(&seed_bytes).unwrap();
+        let snap = driver.snapshot();
+        let peer_hex: String = peer_key.as_bytes().iter().map(|b| format!("{:02x}", b)).collect();
+        let alive = snap
+            .members
+            .iter()
+            .any(|m| m.node_id == peer_hex && m.state == "alive");
+        if alive {
+            eprintln!("cluster converged — remote node is alive");
+            converged = true;
+            break;
+        }
+        std::thread::sleep(Duration::from_millis(50));
+    }
+    if !converged {
+        eprintln!("cluster did not converge within 30s");
+        driver.shutdown();
+        std::process::exit(1);
+    }
+
+    // Wait for name resolution — the remote node registers "inference"
+    eprintln!("resolving name 'inference'...");
+    let start = Instant::now();
+    let mut bridge_addr = None;
+    while start.elapsed() < Duration::from_secs(30) {
+        driver.recv();
+        driver.tick();
+
+        if let Some((addr, _node_id)) = driver.node().resolve_name("inference") {
+            eprintln!("resolved 'inference' -> {:?}", addr);
+            bridge_addr = Some(addr);
+            break;
+        }
+        std::thread::sleep(Duration::from_millis(100));
+    }
+    let bridge_addr = bridge_addr.expect("failed to resolve 'inference' name within 30s");
+
+    // Create actor runtime and response inbox
+    let mut rt = Runtime::new(RuntimeConfig::default());
+    let codecs = Arc::new(inference_codec_registry());
+    let response_inbox = rt.new_inbox::<InferenceResponse>().unwrap();
+    let inbox_addr = *response_inbox.addr();
+
+    // Build transport to the remote node
+    let transport_to_remote = Arc::new(IrohActorTransport::new(
+        driver.endpoint().clone(),
+        seed_addr,
+        driver.tokio_handle(),
+    ));
+
+    // Wire routes: send to bridge_addr on remote
+    let router = TransportRouter::new();
+    router.add_route(bridge_addr, transport_to_remote);
+
+    rt.set_codec_registry(codecs.clone());
+    rt.set_transport_router(Arc::new(router));
+
+    // Send InferenceRequest
+    eprintln!("sending InferenceRequest...");
+    rt.send_to(
+        bridge_addr,
+        InferenceRequest {
+            prompt: "Say hello".into(),
+            max_tokens: 32,
+            temperature: 0.7,
+            reply_to: inbox_addr,
+        },
+    )
+    .unwrap();
+
+    // Pump loop: drain messages, tick, check for response
+    let start = Instant::now();
+    let timeout = Duration::from_secs(300); // generous for model download + inference
+    let mut got_response = false;
+
+    while start.elapsed() < timeout {
+        std::thread::sleep(Duration::from_millis(100));
+
+        driver.recv();
+        driver.tick();
+        drain_actor_messages(&driver, &codecs, &rt, Duration::from_millis(100));
+        rt.tick();
+
+        if let Some(response) = response_inbox.try_recv() {
+            if response.text.is_empty() {
+                eprintln!("received empty response (worker may not be ready), retrying...");
+                continue;
+            }
+            println!("=== Inference Response ===");
+            println!("{}", response.text);
+            println!("==========================");
+            got_response = true;
+            break;
+        }
+    }
+
+    if !got_response {
+        eprintln!("did not receive InferenceResponse within timeout");
+        driver.shutdown();
+        std::process::exit(1);
+    }
+
+    driver.shutdown();
+    eprintln!("single-gpu-inference complete");
+}
+
+fn run_vastai(api_key_raw: String, gpu_name: &str, image: &str) {
+    let api_key = api_key_raw.trim().to_string();
+    // Create a tokio runtime for the async vast.ai client
+    let tokio_rt = tokio::runtime::Runtime::new().expect("failed to create tokio runtime");
+
+    // Create local iroh driver first so we know our node id
+    // Use RelayMode::Default for WAN NAT traversal to vast.ai instances
+    let mut driver = IrohDriver::new(IrohDriverConfig {
+        secret_key: None,
+        relay_mode: RelayMode::Default,
+        node: node_config(),
+        peer_auth: None,
+        additional_alpns: vec![ACTOR_ALPN.to_vec()],
+    })
+    .expect("failed to create iroh driver");
+
+    let my_id = driver.node_id();
+    let my_hex: String = my_id.0.iter().map(|b| format!("{:02x}", b)).collect();
+    eprintln!("single-gpu-inference started (node id: {my_hex})");
+
+    let base_url = "https://cloud.vast.ai";
+    let client = reqwest::Client::new();
+
+    // Wait for relay connection so the remote gpu-node can find us over the internet
+    eprintln!("waiting for relay connection...");
+    let relay_url = {
+        let start = Instant::now();
+        loop {
+            driver.recv();
+            driver.tick();
+            if let Some(url) = driver.home_relay_url() {
+                eprintln!("home relay: {url}");
+                break Some(url.to_string());
+            }
+            if start.elapsed() > Duration::from_secs(15) {
+                eprintln!("warning: no relay URL available after 15s");
+                break None;
+            }
+            std::thread::sleep(Duration::from_millis(100));
+        }
+    };
+
+    // Retry loop: find offer → create → wait for running → wait for convergence.
+    // Broken hosts (CDI errors, GFW, networking) get excluded on retry.
+    let max_retries = 3;
+    let mut excluded_offers: Vec<u64> = Vec::new();
+    let mut running_contract_id = 0u64;
+    let mut converged = false;
+
+    for attempt in 1..=max_retries {
+        // 1. Find cheapest offer (excluding previously failed ones)
+        eprintln!("finding {gpu_name} offer on vast.ai (attempt {attempt}/{max_retries})...");
+        let offer = match tokio_rt.block_on(single_gpu_inference::vastai::find_offer(
+            &client, base_url, &api_key, gpu_name, &excluded_offers,
+        )) {
+            Ok(o) => o,
+            Err(e) => {
+                eprintln!("no offers available: {e}");
+                driver.shutdown();
+                std::process::exit(1);
+            }
+        };
+        eprintln!(
+            "found offer {} ({}, ${:.3}/hr, geo={:?})",
+            offer.id, offer.gpu_name, offer.dph_total, offer.geolocation
+        );
+        let offer_id = offer.id;
+
+        // 2. Create instance with our node id as SEED_ADDR
+        eprintln!("creating instance...");
+        let instance = match tokio_rt.block_on(single_gpu_inference::vastai::create_instance(
+            &client, base_url, &api_key, offer_id, &my_hex,
+            relay_url.as_deref(), image,
+        )) {
+            Ok(i) => i,
+            Err(e) => {
+                eprintln!("failed to create instance: {e}");
+                excluded_offers.push(offer_id);
+                continue;
+            }
+        };
+        let contract_id = instance.contract_id;
+        eprintln!("instance created (contract: {contract_id})");
+
+        // 3. Wait for instance to be running
+        eprintln!("waiting for instance to start...");
+        match tokio_rt.block_on(single_gpu_inference::vastai::wait_for_running(
+            &client, base_url, &api_key, contract_id,
+            Duration::from_secs(10), 60,
+        )) {
+            Ok(r) => {
+                eprintln!("instance running at {}:{}", r.ip, r.port);
+                running_contract_id = contract_id;
+            }
+            Err(e) => {
+                eprintln!("instance failed to start: {e}");
+                eprintln!("destroying instance and excluding offer {offer_id}...");
+                let _ = tokio_rt.block_on(single_gpu_inference::vastai::destroy_instance(
+                    &client, base_url, &api_key, contract_id,
+                ));
+                excluded_offers.push(offer_id);
+                continue;
+            }
+        }
+
+        // 4. Wait for SWIM convergence (gpu-node must connect back via iroh relay)
+        eprintln!("waiting for cluster convergence...");
+        let start = Instant::now();
+        let mut last_log = Instant::now();
+        while start.elapsed() < Duration::from_secs(120) {
+            driver.recv();
+            driver.tick();
+
+            let snap = driver.snapshot();
+            if snap.members.iter().any(|m| m.state == "alive") {
+                eprintln!("cluster converged");
+                converged = true;
+                break;
+            }
+            if last_log.elapsed() >= Duration::from_secs(15) {
+                let elapsed = start.elapsed().as_secs();
+                let members: Vec<_> = snap.members.iter()
+                    .map(|m| format!("{}={}", &m.node_id[..8], m.state))
+                    .collect();
+                eprintln!("  convergence: {elapsed}s elapsed, members: {members:?}");
+                last_log = Instant::now();
+            }
+            std::thread::sleep(Duration::from_millis(100));
+        }
+
+        if converged {
+            break;
+        }
+
+        // Convergence failed — fetch logs for debugging, then destroy
+        eprintln!("cluster did not converge, fetching instance logs...");
+        if let Ok(log_url) = tokio_rt.block_on(single_gpu_inference::vastai::request_logs(
+            &client, base_url, &api_key, contract_id,
+        )) {
+            if let Ok(logs) = tokio_rt.block_on(single_gpu_inference::vastai::fetch_logs(&client, &log_url)) {
+                eprintln!("--- instance logs (contract {contract_id}) ---");
+                // Print last 40 lines to avoid flooding
+                let lines: Vec<&str> = logs.lines().collect();
+                let start = if lines.len() > 40 { lines.len() - 40 } else { 0 };
+                for line in &lines[start..] {
+                    eprintln!("  {line}");
+                }
+                eprintln!("--- end logs ---");
+            }
+        }
+        eprintln!("destroying instance and excluding offer {offer_id}...");
+        let _ = tokio_rt.block_on(single_gpu_inference::vastai::destroy_instance(
+            &client, base_url, &api_key, contract_id,
+        ));
+        excluded_offers.push(offer_id);
+    }
+
+    if !converged {
+        eprintln!("all {max_retries} attempts failed (no host converged), giving up");
+        driver.shutdown();
+        std::process::exit(1);
+    }
+
+    // Resolve "inference" name
+    eprintln!("resolving 'inference' name...");
+    let start = Instant::now();
+    let mut bridge_addr = None;
+    let mut remote_node_id = None;
+    while start.elapsed() < Duration::from_secs(60) {
+        driver.recv();
+        driver.tick();
+        if let Some((addr, node_id)) = driver.node().resolve_name("inference") {
+            bridge_addr = Some(addr);
+            remote_node_id = Some(node_id);
+            break;
+        }
+        std::thread::sleep(Duration::from_millis(200));
+    }
+    let bridge_addr = bridge_addr.expect("failed to resolve 'inference' name");
+    let remote_node_id = remote_node_id.unwrap();
+
+    // Build transport to remote
+    let remote_key = PublicKey::from_bytes(&remote_node_id.0).expect("invalid remote node key");
+    let remote_endpoint_addr = iroh::EndpointAddr::from(remote_key);
+
+    let mut rt = Runtime::new(RuntimeConfig::default());
+    let codecs = Arc::new(inference_codec_registry());
+    let response_inbox = rt.new_inbox::<InferenceResponse>().unwrap();
+    let inbox_addr = *response_inbox.addr();
+
+    let transport_to_remote = Arc::new(IrohActorTransport::new(
+        driver.endpoint().clone(),
+        remote_endpoint_addr,
+        driver.tokio_handle(),
+    ));
+    let router = TransportRouter::new();
+    router.add_route(bridge_addr, transport_to_remote);
+    rt.set_codec_registry(codecs.clone());
+    rt.set_transport_router(Arc::new(router));
+
+    // Send request
+    eprintln!("sending InferenceRequest...");
+    rt.send_to(
+        bridge_addr,
+        InferenceRequest {
+            prompt: "Say hello".into(),
+            max_tokens: 32,
+            temperature: 0.7,
+            reply_to: inbox_addr,
+        },
+    )
+    .unwrap();
+
+    // Pump loop
+    let start = Instant::now();
+    let mut last_diag = Instant::now();
+    let mut got_response = false;
+    while start.elapsed() < Duration::from_secs(300) {
+        std::thread::sleep(Duration::from_millis(100));
+        driver.recv();
+        driver.tick();
+        drain_actor_messages(&driver, &codecs, &rt, Duration::from_millis(50));
+        rt.tick();
+
+        // Periodic SWIM health diagnostics
+        if last_diag.elapsed() >= Duration::from_secs(15) {
+            let snap = driver.snapshot();
+            let members: Vec<_> = snap.members.iter()
+                .map(|m| format!("{}={}", &m.node_id[..8], m.state))
+                .collect();
+            let elapsed = start.elapsed().as_secs();
+            eprintln!("  inference wait: {elapsed}s elapsed, members: {members:?}");
+            last_diag = Instant::now();
+        }
+
+        if let Some(response) = response_inbox.try_recv() {
+            if response.text.is_empty() {
+                continue;
+            }
+            println!("=== Inference Response ===");
+            println!("{}", response.text);
+            println!("==========================");
+            got_response = true;
+            break;
+        }
+    }
+
+    if !got_response {
+        eprintln!("did not receive InferenceResponse within timeout");
+        // Fetch logs for debugging before destroying
+        eprintln!("fetching instance logs...");
+        if let Ok(log_url) = tokio_rt.block_on(single_gpu_inference::vastai::request_logs(
+            &client, base_url, &api_key, running_contract_id,
+        )) {
+            if let Ok(logs) = tokio_rt.block_on(single_gpu_inference::vastai::fetch_logs(&client, &log_url)) {
+                eprintln!("--- instance logs (contract {running_contract_id}) ---");
+                let lines: Vec<&str> = logs.lines().collect();
+                let tail = if lines.len() > 60 { lines.len() - 60 } else { 0 };
+                for line in &lines[tail..] {
+                    eprintln!("  {line}");
+                }
+                eprintln!("--- end logs ---");
+            }
+        }
+    }
+
+    // 5. Destroy instance
+    eprintln!("destroying instance...");
+    let _ = tokio_rt.block_on(single_gpu_inference::vastai::destroy_instance(
+        &client,
+        base_url,
+        &api_key,
+        running_contract_id,
+    ));
+
+    driver.shutdown();
+    if got_response {
+        eprintln!("single-gpu-inference complete");
+    } else {
+        std::process::exit(1);
+    }
+}
diff --git a/examples/single-gpu-inference/src/inference_actor.rs b/examples/single-gpu-inference/src/inference_actor.rs
new file mode 100644
index 0000000..3c5799d
--- /dev/null
+++ b/examples/single-gpu-inference/src/inference_actor.rs
@@ -0,0 +1,220 @@
+//! InferenceActor — bridges swactor messaging to a Python child process.
+//!
+//! Spawns a Python worker (e.g. `echo_worker.py` or `tinygrad_worker.py`) via
+//! the process crate and translates `InferenceRequest` messages into stdin JSON,
+//! then parses stdout JSON into `InferenceResponse` replies.
+
+use std::collections::VecDeque;
+
+use swactor::actor::{ActorAddress, ActorInterface, Ctx};
+use swactor::runtime::ExternalSender;
+use swactor_process::{
+    spawn_local_process, ExitStatus, ProcessCommand, ProcessNotification, ProcessSpec,
+};
+
+use crate::messages::{InferenceRequest, InferenceResponse};
+
+// ── Messages ──────────────────────────────────────────────────────────────
+
+/// Union type for messages the InferenceActor can receive.
+#[derive(Clone, Debug)]
+pub enum InferenceActorMsg {
+    /// An inference request from a client.
+    Request(InferenceRequest),
+    /// A forwarded notification from the child process.
+    Process(ProcessNotification),
+}
+
+/// Status notifications emitted to an optional observer address.
+#[derive(Clone, Debug)]
+pub enum InferenceActorStatus {
+    ProcessStarted,
+    WorkerReady { pid: Option<u32> },
+    ProcessExited { status: ExitStatus },
+}
+
+// ── ProcessBridge ─────────────────────────────────────────────────────────
+
+/// Receives `ProcessNotification` from the ProcessActor and forwards it
+/// wrapped as `InferenceActorMsg::Process` to the InferenceActor.
+///
+/// Necessary because swactor actors have a single `Incoming` type — the
+/// ProcessActor sends `ProcessNotification`, but InferenceActor expects
+/// `InferenceActorMsg`.
+struct ProcessBridge {
+    target: ActorAddress,
+}
+
+impl ActorInterface for ProcessBridge {
+    type Incoming = ProcessNotification;
+    type Response = ();
+
+    fn handle(&mut self, ctx: &Ctx, msg: ProcessNotification) {
+        let _ = ctx.send(self.target, InferenceActorMsg::Process(msg));
+    }
+}
+
+// ── RequestBridge ────────────────────────────────────────────────────────
+
+/// Receives `InferenceRequest` from the network and forwards it wrapped as
+/// `InferenceActorMsg::Request` to the InferenceActor.
+///
+/// Necessary because the network codec delivers raw `InferenceRequest`, but
+/// InferenceActor expects `InferenceActorMsg`.
+pub struct RequestBridge {
+    pub target: ActorAddress,
+}
+
+impl ActorInterface for RequestBridge {
+    type Incoming = InferenceRequest;
+    type Response = ();
+
+    fn handle(&mut self, ctx: &Ctx, msg: InferenceRequest) {
+        let _ = ctx.send(self.target, InferenceActorMsg::Request(msg));
+    }
+}
+
+// ── InferenceActor ────────────────────────────────────────────────────────
+
+pub struct InferenceActor {
+    spec: ProcessSpec,
+    sender: ExternalSender,
+    process_addr: Option<ActorAddress>,
+    bridge_addr: Option<ActorAddress>,
+    pending_replies: VecDeque<ActorAddress>,
+    ready: bool,
+    process_alive: bool,
+    worker_pid: Option<u32>,
+    status_addr: Option<ActorAddress>,
+    output_buffer: String,
+}
+
+impl InferenceActor {
+    pub fn new(spec: ProcessSpec, sender: ExternalSender) -> Self {
+        Self {
+            spec,
+            sender,
+            process_addr: None,
+            bridge_addr: None,
+            pending_replies: VecDeque::new(),
+            ready: false,
+            process_alive: false,
+            worker_pid: None,
+            status_addr: None,
+            output_buffer: String::new(),
+        }
+    }
+
+    /// Set an observer address that receives `InferenceActorStatus` updates.
+    pub fn with_status_addr(mut self, addr: ActorAddress) -> Self {
+        self.status_addr = Some(addr);
+        self
+    }
+
+    fn process_output_line(&mut self, ctx: &Ctx, line: &str) {
+        let Ok(val) = serde_json::from_str::<serde_json::Value>(line) else {
+            // Log non-JSON output (Python tracebacks, error messages, etc.)
+            if !line.is_empty() {
+                eprintln!("worker: {line}");
+            }
+            return;
+        };
+
+        if val.get("status").and_then(|v| v.as_str()) == Some("ready") {
+            self.ready = true;
+            let pid = val.get("pid").and_then(|v| v.as_u64()).map(|p| p as u32);
+            self.worker_pid = pid;
+            if let Some(addr) = self.status_addr {
+                let _ = ctx.send(addr, InferenceActorStatus::WorkerReady { pid });
+            }
+        } else if let Some(response) = val.get("response").and_then(|v| v.as_str()) {
+            if let Some(reply_to) = self.pending_replies.pop_front() {
+                let _ = ctx.send(reply_to, InferenceResponse { text: response.to_string() });
+            }
+        } else if let Some(err) = val.get("error").and_then(|v| v.as_str()) {
+            eprintln!("worker error: {err}");
+        }
+    }
+}
+
+impl ActorInterface for InferenceActor {
+    type Incoming = InferenceActorMsg;
+    type Response = ();
+
+    fn on_start(&mut self, ctx: &Ctx) {
+        let proc_addr = spawn_local_process(ctx, &self.sender, self.spec.clone())
+            .expect("failed to spawn worker process");
+
+        let bridge = ProcessBridge { target: ctx.self_addr() };
+        let bridge_addr = ctx.spawn(bridge).expect("failed to spawn process bridge");
+
+        let _ = ctx.send(proc_addr, ProcessCommand::Subscribe { address: bridge_addr });
+
+        self.process_addr = Some(proc_addr);
+        self.bridge_addr = Some(bridge_addr);
+    }
+
+    fn handle(&mut self, ctx: &Ctx, msg: InferenceActorMsg) {
+        match msg {
+            InferenceActorMsg::Request(req) => {
+                if !self.ready || !self.process_alive {
+                    let _ = ctx.send(req.reply_to, InferenceResponse { text: String::new() });
+                    return;
+                }
+                self.pending_replies.push_back(req.reply_to);
+                let json = serde_json::json!({
+                    "prompt": req.prompt,
+                    "max_tokens": req.max_tokens,
+                    "temperature": req.temperature,
+                });
+                let mut data = serde_json::to_vec(&json).unwrap();
+                data.push(b'\n');
+                if let Some(proc_addr) = self.process_addr {
+                    let _ = ctx.send(proc_addr, ProcessCommand::WriteStdin { data });
+                }
+            }
+            InferenceActorMsg::Process(notif) => match notif {
+                ProcessNotification::Started { .. } => {
+                    self.process_alive = true;
+                    if let Some(addr) = self.status_addr {
+                        let _ = ctx.send(addr, InferenceActorStatus::ProcessStarted);
+                    }
+                }
+                ProcessNotification::Output { data, .. } => {
+                    let text = String::from_utf8_lossy(&data);
+                    self.output_buffer.push_str(&text);
+                    while let Some(pos) = self.output_buffer.find('\n') {
+                        let line = self.output_buffer[..pos].to_string();
+                        self.output_buffer = self.output_buffer[pos + 1..].to_string();
+                        self.process_output_line(ctx, line.trim());
+                    }
+                }
+                ProcessNotification::Exited { status, .. } => {
+                    self.process_alive = false;
+                    self.ready = false;
+                    // Drain pending requests with empty responses
+                    for reply_to in self.pending_replies.drain(..) {
+                        let _ = ctx.send(reply_to, InferenceResponse { text: String::new() });
+                    }
+                    if let Some(addr) = self.status_addr {
+                        let _ = ctx.send(addr, InferenceActorStatus::ProcessExited { status });
+                    }
+                }
+                ProcessNotification::Error { .. } => {
+                    self.process_alive = false;
+                    self.ready = false;
+                }
+            },
+        }
+    }
+
+    fn on_stop(&mut self, ctx: &Ctx) {
+        if let Some(proc_addr) = self.process_addr {
+            let _ = ctx.send(proc_addr, ProcessCommand::Close);
+            let _ = ctx.stop_actor(proc_addr);
+        }
+        if let Some(bridge_addr) = self.bridge_addr {
+            let _ = ctx.stop_actor(bridge_addr);
+        }
+    }
+}
diff --git a/examples/single-gpu-inference/src/iroh_transport.rs b/examples/single-gpu-inference/src/iroh_transport.rs
new file mode 100644
index 0000000..c40197f
--- /dev/null
+++ b/examples/single-gpu-inference/src/iroh_transport.rs
@@ -0,0 +1,161 @@
+//! Shared iroh transport infrastructure for actor-level QUIC messaging.
+//!
+//! Provides `IrohActorTransport` (sends `WireEnvelope`s over iroh QUIC
+//! uni-directional streams), wire encoding/decoding, and a drain helper
+//! that feeds incoming messages into a swactor `Runtime`.
+
+use std::time::Duration;
+
+use distribution::iroh_driver::IrohDriver;
+use swactor::actor::ActorAddress;
+use swactor::runtime::Runtime;
+use swactor::transport::{CodecRegistry, Transport, WireEnvelope};
+use swactor::Error;
+
+/// ALPN protocol for actor-level messages (distinct from SWIM protocol).
+pub const ACTOR_ALPN: &[u8] = b"swactor/actor/1";
+
+/// Sends WireEnvelopes over iroh QUIC uni-directional streams.
+///
+/// Caches the QUIC connection so it stays alive between sends — dropping
+/// a connection before the receiver reads its streams causes "closed by
+/// peer" errors.
+///
+/// Wire format: [32B dest_addr][4B tag_len BE][tag bytes][payload bytes]
+pub struct IrohActorTransport {
+    endpoint: iroh::Endpoint,
+    target_addr: iroh::EndpointAddr,
+    handle: tokio::runtime::Handle,
+    conn: std::sync::Mutex<Option<iroh::endpoint::Connection>>,
+}
+
+impl IrohActorTransport {
+    pub fn new(
+        endpoint: iroh::Endpoint,
+        target_addr: iroh::EndpointAddr,
+        handle: tokio::runtime::Handle,
+    ) -> Self {
+        Self {
+            endpoint,
+            target_addr,
+            handle,
+            conn: std::sync::Mutex::new(None),
+        }
+    }
+}
+
+impl Transport for IrohActorTransport {
+    fn send(&self, envelope: WireEnvelope) -> Result<(), Error> {
+        let data = encode_wire(&envelope);
+        let ep = self.endpoint.clone();
+        let target = self.target_addr.clone();
+        let cached = self.conn.lock().unwrap().clone();
+
+        let conn = self.handle.block_on(async move {
+            if let Some(c) = cached {
+                if c.close_reason().is_none() {
+                    return Ok(c);
+                }
+            }
+            ep.connect(target, ACTOR_ALPN)
+                .await
+                .map_err(|e| Error::from(format!("iroh connect: {e}")))
+        })?;
+
+        *self.conn.lock().unwrap() = Some(conn.clone());
+
+        self.handle.block_on(async move {
+            let mut stream = conn
+                .open_uni()
+                .await
+                .map_err(|e| Error::from(format!("iroh open_uni: {e}")))?;
+            stream
+                .write_all(&data)
+                .await
+                .map_err(|e| Error::from(format!("iroh write: {e}")))?;
+            stream
+                .finish()
+                .map_err(|e| Error::from(format!("iroh finish: {e}")))?;
+            Ok(())
+        })
+    }
+}
+
+pub fn encode_wire(env: &WireEnvelope) -> Vec<u8> {
+    let tag = env.type_tag.as_bytes();
+    let mut buf = Vec::with_capacity(32 + 4 + tag.len() + env.payload.len());
+    buf.extend_from_slice(&env.dest.0);
+    buf.extend_from_slice(&(tag.len() as u32).to_be_bytes());
+    buf.extend_from_slice(tag);
+    buf.extend_from_slice(&env.payload);
+    buf
+}
+
+pub fn decode_wire(data: &[u8]) -> Option<WireEnvelope> {
+    if data.len() < 36 {
+        return None;
+    }
+    let mut addr = [0u8; 32];
+    addr.copy_from_slice(&data[..32]);
+    let tag_len = u32::from_be_bytes(data[32..36].try_into().ok()?) as usize;
+    if data.len() < 36 + tag_len {
+        return None;
+    }
+    let type_tag = String::from_utf8(data[36..36 + tag_len].to_vec()).ok()?;
+    let payload = data[36 + tag_len..].to_vec();
+    Some(WireEnvelope {
+        dest: ActorAddress(addr),
+        type_tag,
+        payload,
+    })
+}
+
+/// Drain actor messages arriving via iroh into a swactor runtime.
+///
+/// Spawns tokio tasks (on the driver's runtime) to read uni-directional
+/// streams from pending actor-ALPN connections. Results are collected
+/// via a channel and delivered to the swactor runtime.
+///
+/// `drain_sleep` controls how long to wait for spawned tasks to read
+/// streams before collecting results. Use shorter durations (e.g. 100ms)
+/// for localhost tests, longer (e.g. 500ms) for cross-node scenarios.
+pub fn drain_actor_messages(
+    driver: &IrohDriver,
+    codecs: &CodecRegistry,
+    rt: &Runtime,
+    drain_sleep: Duration,
+) {
+    let conns = driver.drain_other_connections();
+    if conns.is_empty() {
+        return;
+    }
+    let handle = driver.tokio_handle();
+    let (tx, rx) = std::sync::mpsc::channel::<Vec<u8>>();
+
+    for (_node_id, conn) in conns {
+        let tx = tx.clone();
+        handle.spawn(async move {
+            loop {
+                match tokio::time::timeout(Duration::from_secs(1), conn.accept_uni()).await {
+                    Ok(Ok(mut recv)) => {
+                        if let Ok(data) = recv.read_to_end(256 * 1024).await {
+                            let _ = tx.send(data);
+                        }
+                    }
+                    _ => break,
+                }
+            }
+        });
+    }
+    drop(tx);
+
+    std::thread::sleep(drain_sleep);
+
+    for data in rx.try_iter() {
+        if let Some(envelope) = decode_wire(&data) {
+            if let Ok((addr, msg)) = codecs.receive(envelope) {
+                let _ = rt.deliver_raw(addr, msg);
+            }
+        }
+    }
+}
diff --git a/examples/single-gpu-inference/src/lib.rs b/examples/single-gpu-inference/src/lib.rs
new file mode 100644
index 0000000..4315e61
--- /dev/null
+++ b/examples/single-gpu-inference/src/lib.rs
@@ -0,0 +1,4 @@
+pub mod inference_actor;
+pub mod iroh_transport;
+pub mod messages;
+pub mod vastai;
diff --git a/examples/single-gpu-inference/src/messages.rs b/examples/single-gpu-inference/src/messages.rs
new file mode 100644
index 0000000..a3371b4
--- /dev/null
+++ b/examples/single-gpu-inference/src/messages.rs
@@ -0,0 +1,67 @@
+//! Inference message types, codec, and registry for the smoke test.
+
+use serde::{Deserialize, Serialize};
+use swactor::actor::ActorAddress;
+use swactor::transport::{Codec, CodecRegistry, NetworkMessage};
+use swactor::Error;
+
+// ─── Message Types ─────────────────────────────────────────────────────────
+
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
+pub struct InferenceRequest {
+    pub prompt: String,
+    pub max_tokens: u32,
+    pub temperature: f32,
+    pub reply_to: ActorAddress,
+}
+
+impl NetworkMessage for InferenceRequest {
+    fn type_tag() -> &'static str {
+        "smoke::InferenceRequest"
+    }
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
+pub struct InferenceResponse {
+    pub text: String,
+}
+
+impl NetworkMessage for InferenceResponse {
+    fn type_tag() -> &'static str {
+        "smoke::InferenceResponse"
+    }
+}
+
+// ─── JSON Codec ────────────────────────────────────────────────────────────
+
+pub struct InferenceCodec;
+
+impl Codec<InferenceRequest> for InferenceCodec {
+    fn encode(&self, msg: &InferenceRequest) -> Result<Vec<u8>, Error> {
+        serde_json::to_vec(msg).map_err(|e| Error::from(format!("encode InferenceRequest: {e}")))
+    }
+    fn decode(&self, bytes: &[u8]) -> Result<InferenceRequest, Error> {
+        serde_json::from_slice(bytes)
+            .map_err(|e| Error::from(format!("decode InferenceRequest: {e}")))
+    }
+}
+
+impl Codec<InferenceResponse> for InferenceCodec {
+    fn encode(&self, msg: &InferenceResponse) -> Result<Vec<u8>, Error> {
+        serde_json::to_vec(msg).map_err(|e| Error::from(format!("encode InferenceResponse: {e}")))
+    }
+    fn decode(&self, bytes: &[u8]) -> Result<InferenceResponse, Error> {
+        serde_json::from_slice(bytes)
+            .map_err(|e| Error::from(format!("decode InferenceResponse: {e}")))
+    }
+}
+
+// ─── Registry ──────────────────────────────────────────────────────────────
+
+/// Build a `CodecRegistry` with inference message types registered.
+pub fn inference_codec_registry() -> CodecRegistry {
+    let mut cr = CodecRegistry::new();
+    cr.register::<InferenceRequest, _>(InferenceCodec);
+    cr.register::<InferenceResponse, _>(InferenceCodec);
+    cr
+}
diff --git a/examples/single-gpu-inference/src/vastai.rs b/examples/single-gpu-inference/src/vastai.rs
new file mode 100644
index 0000000..5bc345d
--- /dev/null
+++ b/examples/single-gpu-inference/src/vastai.rs
@@ -0,0 +1,292 @@
+//! vast.ai REST API client for the smoke test orchestrator.
+//!
+//! Functions: find_offer, create_instance, wait_for_running, destroy_instance.
+//! All functions accept a `base_url` parameter so tests can point at a mock server.
+
+use reqwest::Client;
+use serde::Deserialize;
+use std::time::Duration;
+
+#[derive(Debug, Clone, Deserialize)]
+pub struct Offer {
+    pub id: u64,
+    pub gpu_name: String,
+    pub dph_total: f64,
+    #[serde(default)]
+    pub geolocation: Option<String>,
+}
+
+#[derive(Debug, Clone)]
+pub struct InstanceInfo {
+    pub contract_id: u64,
+}
+
+#[derive(Debug, Clone)]
+pub struct RunningInstance {
+    pub ip: String,
+    pub port: u16,
+}
+
+#[derive(Debug, Deserialize)]
+struct SearchResponse {
+    offers: Vec<Offer>,
+}
+
+#[derive(Debug, Deserialize)]
+struct CreateResponse {
+    new_contract: u64,
+}
+
+#[derive(Debug, Deserialize)]
+struct InstanceResponse {
+    instances: InstanceStatus,
+}
+
+#[derive(Debug, Deserialize)]
+struct InstanceStatus {
+    actual_status: Option<String>,
+    intended_status: Option<String>,
+    #[serde(default)]
+    status_msg: Option<String>,
+    #[serde(default)]
+    public_ipaddr: Option<String>,
+    #[serde(default)]
+    ssh_port: Option<u16>,
+}
+
+/// Find the cheapest offer matching a GPU type, excluding specific offer IDs.
+pub async fn find_offer(
+    client: &Client,
+    base_url: &str,
+    api_key: &str,
+    gpu_name: &str,
+    exclude_ids: &[u64],
+) -> Result<Offer, String> {
+    let query = serde_json::json!({
+        "gpu_name": {"eq": gpu_name},
+        "rentable": {"eq": true},
+        "rented": {"eq": false},
+        "reliability2": {"gte": 0.99},
+        "cuda_max_good": {"gte": 12.0},
+        "verified": {"eq": true},
+        "direct_port_count": {"gte": 1},
+        "inet_down": {"gte": 100.0},
+    });
+    let url = format!(
+        "{base_url}/api/v0/bundles/?q={}",
+        urlencoding::encode(&query.to_string())
+    );
+    let resp = client
+        .get(&url)
+        .header("Authorization", format!("Bearer {api_key}"))
+        .send()
+        .await
+        .map_err(|e| format!("find_offer request failed: {e}"))?;
+
+    if !resp.status().is_success() {
+        let status = resp.status();
+        let body = resp.text().await.unwrap_or_default();
+        return Err(format!("find_offer HTTP {status}: {body}"));
+    }
+
+    let body: SearchResponse = resp
+        .json()
+        .await
+        .map_err(|e| format!("find_offer parse failed: {e}"))?;
+
+    // Filter out hosts with unknown or Chinese geolocation — Docker Hub
+    // and iroh relays are unreachable from behind the Great Firewall.
+    let filtered: Vec<Offer> = body
+        .offers
+        .into_iter()
+        .filter(|o| {
+            o.geolocation
+                .as_deref()
+                .map_or(false, |g| !g.to_uppercase().contains("CN"))
+        })
+        .collect();
+
+    let candidates: Vec<Offer> = filtered
+        .into_iter()
+        .filter(|o| !exclude_ids.contains(&o.id))
+        .collect();
+
+    candidates
+        .into_iter()
+        .min_by(|a, b| a.dph_total.partial_cmp(&b.dph_total).unwrap())
+        .ok_or_else(|| "no offers available (after geo/exclusion filter)".to_string())
+}
+
+/// Create a vast.ai instance from an offer, passing SEED_ADDR and SEED_RELAY in the env.
+pub async fn create_instance(
+    client: &Client,
+    base_url: &str,
+    api_key: &str,
+    offer_id: u64,
+    seed_addr: &str,
+    seed_relay: Option<&str>,
+    image: &str,
+) -> Result<InstanceInfo, String> {
+    let url = format!("{base_url}/api/v0/asks/{offer_id}/");
+    let mut env = serde_json::json!({ "SEED_ADDR": seed_addr });
+    if let Some(relay) = seed_relay {
+        env["SEED_RELAY"] = serde_json::Value::String(relay.to_string());
+    }
+    let body = serde_json::json!({
+        "image": image,
+        "env": env,
+        "onstart": "exec /usr/local/bin/gpu-node 2>&1",
+        "disk": 20,
+    });
+
+    let resp = client
+        .put(&url)
+        .header("Authorization", format!("Bearer {api_key}"))
+        .json(&body)
+        .send()
+        .await
+        .map_err(|e| format!("create_instance request failed: {e}"))?;
+
+    if !resp.status().is_success() {
+        let status = resp.status();
+        let body = resp.text().await.unwrap_or_default();
+        return Err(format!("create_instance HTTP {status}: {body}"));
+    }
+
+    let parsed: CreateResponse = resp
+        .json()
+        .await
+        .map_err(|e| format!("create_instance parse failed: {e}"))?;
+
+    Ok(InstanceInfo {
+        contract_id: parsed.new_contract,
+    })
+}
+
+/// Poll vast.ai until the instance reaches `running`, then extract IP + port.
+/// Returns error immediately on terminal statuses like `exited`.
+pub async fn wait_for_running(
+    client: &Client,
+    base_url: &str,
+    api_key: &str,
+    contract_id: u64,
+    poll_interval: Duration,
+    max_polls: u32,
+) -> Result<RunningInstance, String> {
+    let url = format!("{base_url}/api/v0/instances/{contract_id}/");
+
+    for poll in 0..max_polls {
+        let resp = client
+            .get(&url)
+            .header("Authorization", format!("Bearer {api_key}"))
+            .send()
+            .await
+            .map_err(|e| format!("wait_for_running request failed: {e}"))?;
+
+        if !resp.status().is_success() {
+            let status = resp.status();
+            let body = resp.text().await.unwrap_or_default();
+            return Err(format!("wait_for_running HTTP {status}: {body}"));
+        }
+
+        let wrapper: InstanceResponse = resp
+            .json()
+            .await
+            .map_err(|e| format!("wait_for_running parse failed: {e}"))?;
+        let status = wrapper.instances;
+
+        let actual = status.actual_status.as_deref().unwrap_or("unknown");
+        let intended = status.intended_status.as_deref().unwrap_or("unknown");
+        eprintln!("  poll {}/{}: status={actual}", poll + 1, max_polls);
+
+        // Check for error in status_msg (host-side failures like OCI errors)
+        if let Some(msg) = &status.status_msg {
+            if msg.contains("Error") || msg.contains("failed") {
+                return Err(format!("instance error: {msg}"));
+            }
+        }
+
+        // Check if intended_status has gone to stopped (instance gave up)
+        if intended == "stopped" && actual != "running" {
+            let msg = status.status_msg.unwrap_or_default();
+            return Err(format!("instance stopped: {msg}"));
+        }
+
+        match actual {
+            "running" => {
+                let ip = status
+                    .public_ipaddr
+                    .unwrap_or_else(|| "unknown".to_string());
+                let port = status.ssh_port.unwrap_or(0);
+                return Ok(RunningInstance { ip, port });
+            }
+            "exited" | "error" => {
+                return Err(format!("instance reached terminal status: {actual}"));
+            }
+            _ => {
+                tokio::time::sleep(poll_interval).await;
+            }
+        }
+    }
+
+    Err("instance did not reach running within poll limit".to_string())
+}
+
+/// Request instance logs and return the download URL.
+/// Logs take a few seconds to become available after this call.
+pub async fn request_logs(
+    client: &Client,
+    base_url: &str,
+    api_key: &str,
+    contract_id: u64,
+) -> Result<String, String> {
+    let url = format!("{base_url}/api/v0/instances/request_logs/{contract_id}/");
+    let resp = client
+        .put(&url)
+        .header("Authorization", format!("Bearer {api_key}"))
+        .send()
+        .await
+        .map_err(|e| format!("request_logs failed: {e}"))?;
+
+    let body: serde_json::Value = resp
+        .json()
+        .await
+        .map_err(|e| format!("request_logs parse failed: {e}"))?;
+
+    body["result_url"]
+        .as_str()
+        .map(|s| s.to_string())
+        .ok_or_else(|| "no result_url in log response".to_string())
+}
+
+/// Fetch instance logs from S3 URL. Returns the log text.
+pub async fn fetch_logs(client: &Client, log_url: &str) -> Result<String, String> {
+    // Wait for the log to become available
+    tokio::time::sleep(std::time::Duration::from_secs(5)).await;
+    let resp = client
+        .get(log_url)
+        .send()
+        .await
+        .map_err(|e| format!("fetch_logs failed: {e}"))?;
+    resp.text()
+        .await
+        .map_err(|e| format!("fetch_logs read failed: {e}"))
+}
+
+/// Destroy a vast.ai instance.
+pub async fn destroy_instance(
+    client: &Client,
+    base_url: &str,
+    api_key: &str,
+    contract_id: u64,
+) -> Result<(), String> {
+    let url = format!("{base_url}/api/v0/instances/{contract_id}/");
+    client
+        .delete(&url)
+        .header("Authorization", format!("Bearer {api_key}"))
+        .send()
+        .await
+        .map_err(|e| format!("destroy_instance request failed: {e}"))?;
+
+    Ok(())
+}
diff --git a/examples/single-gpu-inference/tests/t_actor.rs b/examples/single-gpu-inference/tests/t_actor.rs
new file mode 100644
index 0000000..68156a5
--- /dev/null
+++ b/examples/single-gpu-inference/tests/t_actor.rs
@@ -0,0 +1,215 @@
+//! T-actor: InferenceActor + process bridge component tests.
+//!
+//! Spawns the InferenceActor on a single swactor runtime with `echo_worker.py`
+//! as the child process. No networking, no GPU.
+
+use std::collections::HashMap;
+use std::time::{Duration, Instant};
+
+use swactor::actor::ActorAddress;
+use swactor::runtime::{Inbox, Runtime, RuntimeConfig};
+
+use single_gpu_inference::inference_actor::{InferenceActor, InferenceActorMsg, InferenceActorStatus};
+use single_gpu_inference::messages::{InferenceRequest, InferenceResponse};
+use swactor_process::{ExitStatus, ProcessMode, ProcessSpec};
+
+// ── Helpers ───────────────────────────────────────────────────────────────
+
+fn echo_worker_spec() -> ProcessSpec {
+    ProcessSpec {
+        command: "python3".into(),
+        args: vec![format!("{}/echo_worker.py", env!("CARGO_MANIFEST_DIR"))],
+        env: HashMap::new(),
+        working_dir: None,
+        mode: ProcessMode::Automated,
+        initial_pty_size: None,
+        kill_timeout: Some(Duration::from_secs(2)),
+        stdin_buffer_limit: None,
+    }
+}
+
+/// Tick the runtime in a polling loop until the inbox has a message or timeout.
+fn tick_until_recv<M: swactor::actor::Message>(
+    rt: &Runtime,
+    inbox: &Inbox<M>,
+    timeout: Duration,
+) -> Option<M> {
+    let start = Instant::now();
+    while start.elapsed() < timeout {
+        rt.tick();
+        if let Some(msg) = inbox.try_recv() {
+            return Some(msg);
+        }
+        std::thread::sleep(Duration::from_millis(5));
+    }
+    None
+}
+
+/// Spin up an InferenceActor and wait until it reports WorkerReady.
+/// Returns (actor address, worker PID).
+fn spawn_and_wait_ready(
+    rt: &Runtime,
+    status_inbox: &Inbox<InferenceActorStatus>,
+) -> (ActorAddress, u32) {
+    let sender = rt.create_sender();
+    let actor = InferenceActor::new(echo_worker_spec(), sender)
+        .with_status_addr(*status_inbox.addr());
+    let addr = rt.spawn(actor).unwrap();
+
+    let timeout = Duration::from_secs(5);
+    let mut got_started = false;
+    let mut worker_pid = None;
+
+    let start = Instant::now();
+    while start.elapsed() < timeout {
+        if let Some(status) = tick_until_recv(rt, status_inbox, Duration::from_millis(100)) {
+            match status {
+                InferenceActorStatus::ProcessStarted => got_started = true,
+                InferenceActorStatus::WorkerReady { pid } => {
+                    assert!(got_started, "WorkerReady should come after ProcessStarted");
+                    worker_pid = pid;
+                    break;
+                }
+                other => panic!("unexpected status during startup: {:?}", other),
+            }
+        }
+    }
+
+    let pid = worker_pid.expect("worker should report PID within timeout");
+    (addr, pid)
+}
+
+fn is_process_alive(pid: u32) -> bool {
+    std::fs::metadata(format!("/proc/{}", pid)).is_ok()
+}
+
+// ── Tests ─────────────────────────────────────────────────────────────────
+
+#[test]
+fn actor_spawns_process_and_receives_started() {
+    let rt = Runtime::new(RuntimeConfig::default());
+    let status_inbox = rt.new_inbox::<InferenceActorStatus>().unwrap();
+
+    let (_addr, pid) = spawn_and_wait_ready(&rt, &status_inbox);
+
+    // The process should be alive
+    assert!(is_process_alive(pid), "worker process should be running");
+}
+
+#[test]
+fn inference_request_flows_through_process_and_reply_arrives() {
+    let rt = Runtime::new(RuntimeConfig::default());
+    let status_inbox = rt.new_inbox::<InferenceActorStatus>().unwrap();
+    let response_inbox = rt.new_inbox::<InferenceResponse>().unwrap();
+
+    let (addr, _pid) = spawn_and_wait_ready(&rt, &status_inbox);
+
+    // Send an inference request
+    rt.send_to(
+        addr,
+        InferenceActorMsg::Request(InferenceRequest {
+            prompt: "Hello, world!".into(),
+            max_tokens: 8,
+            temperature: 0.7,
+            reply_to: *response_inbox.addr(),
+        }),
+    )
+    .unwrap();
+
+    let response = tick_until_recv(&rt, &response_inbox, Duration::from_secs(5))
+        .expect("should receive InferenceResponse");
+
+    assert!(
+        response.text.contains("Hello, world!"),
+        "echo worker should reflect the prompt, got: {:?}",
+        response.text
+    );
+}
+
+#[test]
+fn worker_crash_is_handled_without_poisoning_runtime() {
+    let rt = Runtime::new(RuntimeConfig::default());
+    let status_inbox = rt.new_inbox::<InferenceActorStatus>().unwrap();
+    let response_inbox = rt.new_inbox::<InferenceResponse>().unwrap();
+
+    let (addr, _pid) = spawn_and_wait_ready(&rt, &status_inbox);
+
+    // Send a request whose prompt triggers an os._exit(1) in the worker
+    rt.send_to(
+        addr,
+        InferenceActorMsg::Request(InferenceRequest {
+            prompt: "__crash__".into(),
+            max_tokens: 1,
+            temperature: 0.0,
+            reply_to: *response_inbox.addr(),
+        }),
+    )
+    .unwrap();
+
+    // The actor should report ProcessExited
+    let status = tick_until_recv(&rt, &status_inbox, Duration::from_secs(5))
+        .expect("should receive ProcessExited status");
+    match status {
+        InferenceActorStatus::ProcessExited { status } => {
+            assert_ne!(
+                status,
+                ExitStatus::Code(0),
+                "crashed worker should not exit 0"
+            );
+        }
+        other => panic!("expected ProcessExited, got: {:?}", other),
+    }
+
+    // Prove the runtime is still alive: spawn a trivial actor and interact with it
+    #[derive(Clone)]
+    struct Ping {
+        reply_to: ActorAddress,
+    }
+    #[derive(Clone, Debug, PartialEq)]
+    struct Pong;
+
+    struct PongActor;
+    impl swactor::actor::ActorInterface for PongActor {
+        type Incoming = Ping;
+        type Response = Pong;
+        fn handle(&mut self, ctx: &swactor::runtime::Ctx, msg: Ping) {
+            let _ = ctx.send(msg.reply_to, Pong);
+        }
+    }
+
+    let pong_inbox = rt.new_inbox::<Pong>().unwrap();
+    let pong_addr = rt.spawn(PongActor).unwrap();
+    rt.send_to(pong_addr, Ping { reply_to: *pong_inbox.addr() }).unwrap();
+
+    let pong = tick_until_recv(&rt, &pong_inbox, Duration::from_secs(2));
+    assert_eq!(pong, Some(Pong), "runtime should still be functional after worker crash");
+}
+
+#[test]
+fn stopping_actor_kills_child_process() {
+    let rt = Runtime::new(RuntimeConfig::default());
+    let status_inbox = rt.new_inbox::<InferenceActorStatus>().unwrap();
+
+    let (addr, pid) = spawn_and_wait_ready(&rt, &status_inbox);
+    assert!(is_process_alive(pid), "worker should be alive before stop");
+
+    // Stop the InferenceActor
+    rt.stop_actor(addr).unwrap();
+
+    // Tick until the child process is gone
+    let start = Instant::now();
+    let timeout = Duration::from_secs(5);
+    while start.elapsed() < timeout {
+        rt.tick();
+        std::thread::sleep(Duration::from_millis(10));
+        if !is_process_alive(pid) {
+            break;
+        }
+    }
+
+    assert!(
+        !is_process_alive(pid),
+        "worker process (pid {}) should be dead after actor stop",
+        pid
+    );
+}
diff --git a/examples/single-gpu-inference/tests/t_binary.rs b/examples/single-gpu-inference/tests/t_binary.rs
new file mode 100644
index 0000000..e277b58
--- /dev/null
+++ b/examples/single-gpu-inference/tests/t_binary.rs
@@ -0,0 +1,164 @@
+//! Binary integration test — validates gpu-node and smoke-run work end-to-end.
+//!
+//! Spawns both binaries as child processes on localhost. gpu-node runs an
+//! InferenceActor backed by a Python worker; smoke-run connects, sends an
+//! InferenceRequest through the distributed pipeline, and receives the response.
+//!
+//! This proves the binaries work through actual process boundaries — the same
+//! code path used in production, unlike the in-process tests in t_integration.rs.
+//!
+//! - `binary_e2e_echo_worker` — fast, uses echo_worker.py (canned echo responses).
+//! - `binary_e2e_tinygrad` — slow (#[ignore]), uses real tinygrad inference.
+
+use std::io::{BufRead, BufReader};
+use std::process::{Child, Command, Stdio};
+use std::time::Duration;
+
+/// Parse the GPU_NODE_ADDR line from gpu-node's stderr.
+/// Format: `GPU_NODE_ADDR <hex_node_id> <ip:port,ip:port,...>`
+fn parse_gpu_node_addr(line: &str) -> Option<(String, String)> {
+    let line = line.trim();
+    if !line.starts_with("GPU_NODE_ADDR ") {
+        return None;
+    }
+    let parts: Vec<&str> = line.splitn(3, ' ').collect();
+    if parts.len() < 3 {
+        return None;
+    }
+    Some((parts[1].to_string(), parts[2].to_string()))
+}
+
+/// Start gpu-node, wait for it to print its address, run smoke-run, verify response.
+fn run_binary_e2e(worker_cmd: &str, worker_script: &str, extra_env: Vec<(&str, &str)>) {
+    let gpu_node_bin = env!("CARGO_BIN_EXE_gpu-node");
+    let smoke_run_bin = env!("CARGO_BIN_EXE_single-gpu-inference");
+
+    // 1. Start gpu-node without SEED_ADDR (it just listens for connections)
+    let mut cmd = Command::new(gpu_node_bin);
+    cmd.env("WORKER_CMD", worker_cmd)
+        .env("WORKER_SCRIPT", worker_script)
+        .env_remove("SEED_ADDR")
+        .stderr(Stdio::piped());
+    for (k, v) in &extra_env {
+        cmd.env(k, v);
+    }
+    let mut gpu_node = cmd.spawn().expect("failed to spawn gpu-node");
+
+    // 2. Read gpu-node stderr in a background thread to find its address
+    //    and keep draining so the pipe buffer doesn't fill up.
+    let gpu_stderr = gpu_node.stderr.take().unwrap();
+    let (addr_tx, addr_rx) = std::sync::mpsc::channel::<(String, String)>();
+    let stderr_thread = std::thread::spawn(move || {
+        let reader = BufReader::new(gpu_stderr);
+        let mut sent = false;
+        for line in reader.lines().flatten() {
+            eprintln!("[gpu-node] {}", line);
+            if !sent {
+                if let Some(addr_info) = parse_gpu_node_addr(&line) {
+                    let _ = addr_tx.send(addr_info);
+                    sent = true;
+                }
+            }
+        }
+    });
+
+    // 3. Wait for gpu-node to print its address
+    let (node_id, direct_addrs) = addr_rx
+        .recv_timeout(Duration::from_secs(30))
+        .expect("gpu-node did not print GPU_NODE_ADDR within 30s");
+    eprintln!("gpu-node ready: node_id={node_id}, direct={direct_addrs}");
+
+    // 4. Spawn smoke-run pointing at gpu-node
+    let mut smoke_run = Command::new(smoke_run_bin)
+        .arg("--seed")
+        .arg(&node_id)
+        .arg("--seed-direct")
+        .arg(&direct_addrs)
+        .stdout(Stdio::piped())
+        .stderr(Stdio::piped())
+        .spawn()
+        .expect("failed to spawn smoke-run");
+
+    // 5. Wait for smoke-run with timeout (poll every 200ms, bail after 120s)
+    let start = std::time::Instant::now();
+    let timeout = Duration::from_secs(120);
+    loop {
+        match smoke_run.try_wait() {
+            Ok(Some(_)) => break,
+            Ok(None) => {
+                if start.elapsed() > timeout {
+                    let _ = smoke_run.kill();
+                    let _ = smoke_run.wait();
+                    cleanup(&mut gpu_node, stderr_thread);
+                    panic!("smoke-run did not exit within {timeout:?}");
+                }
+                std::thread::sleep(Duration::from_millis(200));
+            }
+            Err(e) => {
+                cleanup(&mut gpu_node, stderr_thread);
+                panic!("error waiting for smoke-run: {e}");
+            }
+        }
+    }
+    let output = smoke_run.wait_with_output().expect("failed to read smoke-run output");
+
+    let stdout = String::from_utf8_lossy(&output.stdout);
+    let stderr_out = String::from_utf8_lossy(&output.stderr);
+    eprintln!("=== smoke-run stderr ===\n{stderr_out}");
+    eprintln!("=== smoke-run stdout ===\n{stdout}");
+
+    // 6. Clean up gpu-node
+    cleanup(&mut gpu_node, stderr_thread);
+
+    // 7. Verify
+    assert!(
+        output.status.success(),
+        "smoke-run exited with {:?}",
+        output.status
+    );
+    assert!(
+        stdout.contains("=== Inference Response ==="),
+        "stdout should contain response header"
+    );
+
+    // Extract and verify the response text
+    let response_text: String = stdout
+        .lines()
+        .skip_while(|l| *l != "=== Inference Response ===")
+        .skip(1) // skip the header itself
+        .take_while(|l| *l != "==========================")
+        .collect::<Vec<_>>()
+        .join("\n");
+    assert!(
+        !response_text.is_empty(),
+        "response text between markers should be non-empty"
+    );
+    eprintln!("response: {response_text:?}");
+}
+
+fn cleanup(gpu_node: &mut Child, stderr_thread: std::thread::JoinHandle<()>) {
+    let _ = gpu_node.kill();
+    let _ = gpu_node.wait();
+    let _ = stderr_thread.join();
+}
+
+#[test]
+fn binary_e2e_echo_worker() {
+    let manifest_dir = env!("CARGO_MANIFEST_DIR");
+    let script = format!("{manifest_dir}/echo_worker.py");
+    run_binary_e2e("python3", &script, vec![]);
+}
+
+/// Full binary e2e with real tinygrad inference (~1B GGUF model).
+///
+/// Requires `.venv` with tinygrad installed and downloads a large model.
+/// Run explicitly with:
+///   cargo test --package smoke-test binary_e2e_tinygrad -- --ignored
+#[test]
+#[ignore]
+fn binary_e2e_tinygrad() {
+    let manifest_dir = env!("CARGO_MANIFEST_DIR");
+    let python = format!("{manifest_dir}/.venv/bin/python");
+    let script = format!("{manifest_dir}/tinygrad_worker.py");
+    run_binary_e2e(&python, &script, vec![]);
+}
diff --git a/examples/single-gpu-inference/tests/t_cluster.rs b/examples/single-gpu-inference/tests/t_cluster.rs
new file mode 100644
index 0000000..e6a89ec
--- /dev/null
+++ b/examples/single-gpu-inference/tests/t_cluster.rs
@@ -0,0 +1,270 @@
+//! T-cluster: Cross-node messaging component tests.
+//!
+//! Two swactor nodes on localhost via iroh. Tests SWIM convergence,
+//! actor-level InferenceRequest/InferenceResponse exchange, and
+//! SWIM death detection after node shutdown.
+
+use std::sync::Arc;
+use std::time::{Duration, Instant};
+
+use distribution::iroh_driver::{IrohDriver, IrohDriverConfig};
+use distribution::node::DistributedNodeConfig;
+use distribution::registry::RegistryConfig;
+use distribution::swim::probe::SwimConfig;
+use iroh::{PublicKey, RelayMode};
+
+use swactor::actor::ActorInterface;
+use swactor::runtime::{Ctx, Runtime, RuntimeConfig};
+use swactor::transport::TransportRouter;
+
+use single_gpu_inference::iroh_transport::{drain_actor_messages, IrohActorTransport, ACTOR_ALPN};
+use single_gpu_inference::messages::{inference_codec_registry, InferenceRequest, InferenceResponse};
+
+// ── Test SWIM config ─────────────────────────────────────────────────────
+
+fn test_node_config() -> DistributedNodeConfig {
+    DistributedNodeConfig {
+        swim: SwimConfig {
+            probe_interval: 1,
+            probe_timeout: 3,
+            indirect_probes: 1,
+            suspicion_timeout: 5,
+            dead_reprobe_interval: 0,
+            ..SwimConfig::default()
+        },
+        cache_capacity: 100,
+        republish_interval: 50,
+        registry: RegistryConfig::default(),
+        metadata_lambda: 3,
+    }
+}
+
+fn make_driver() -> IrohDriver {
+    IrohDriver::new(IrohDriverConfig {
+        secret_key: None,
+        relay_mode: RelayMode::Disabled,
+        node: test_node_config(),
+        peer_auth: None,
+        additional_alpns: vec![ACTOR_ALPN.to_vec()],
+    })
+    .expect("failed to create iroh driver")
+}
+
+// ── Pump helpers ─────────────────────────────────────────────────────────
+
+fn pump_one(driver: &mut IrohDriver) {
+    driver.recv();
+    driver.tick();
+}
+
+fn pump_until_pair(
+    a: &mut IrohDriver,
+    b: &mut IrohDriver,
+    timeout: Duration,
+    check_fn: fn(&IrohDriver, &IrohDriver) -> bool,
+) -> bool {
+    let start = Instant::now();
+    while start.elapsed() < timeout {
+        pump_one(a);
+        pump_one(b);
+        if check_fn(a, b) {
+            return true;
+        }
+        std::thread::sleep(Duration::from_millis(10));
+    }
+    false
+}
+
+fn sees_alive(driver: &IrohDriver, peer_key: &PublicKey) -> bool {
+    let snap = driver.snapshot();
+    let peer_hex: String = peer_key
+        .as_bytes()
+        .iter()
+        .map(|b| format!("{:02x}", b))
+        .collect();
+    snap.members
+        .iter()
+        .any(|m| m.node_id == peer_hex && m.state == "alive")
+}
+
+fn both_alive(a: &IrohDriver, b: &IrohDriver) -> bool {
+    let a_key = PublicKey::from_bytes(&a.node_id().0).unwrap();
+    let b_key = PublicKey::from_bytes(&b.node_id().0).unwrap();
+    sees_alive(a, &b_key) && sees_alive(b, &a_key)
+}
+
+/// Create two IrohDrivers and converge them via seed join.
+fn make_converged_pair() -> (IrohDriver, IrohDriver) {
+    let mut driver_a = make_driver();
+    let mut driver_b = make_driver();
+
+    let a_addr = driver_a.endpoint_addr();
+    driver_b.join(&[a_addr]);
+
+    let converged = pump_until_pair(
+        &mut driver_a,
+        &mut driver_b,
+        Duration::from_secs(5),
+        both_alive,
+    );
+    assert!(converged, "cluster setup: nodes did not converge within 5s");
+
+    (driver_a, driver_b)
+}
+
+// ── Echo actor (replies InferenceResponse for any InferenceRequest) ─────
+
+struct EchoInferenceActor;
+
+impl ActorInterface for EchoInferenceActor {
+    type Incoming = InferenceRequest;
+    type Response = InferenceResponse;
+
+    fn handle(&mut self, ctx: &Ctx, msg: InferenceRequest) {
+        let _ = ctx.send(
+            msg.reply_to,
+            InferenceResponse {
+                text: format!("echo: {}", msg.prompt),
+            },
+        );
+    }
+}
+
+// ── Tests ────────────────────────────────────────────────────────────────
+
+/// Node B joins node A via seed address. SWIM converges — both nodes see
+/// each other alive within 5 seconds.
+#[test]
+fn cluster_converges_via_iroh_seed_join() {
+    let mut driver_a = make_driver();
+    let mut driver_b = make_driver();
+
+    let a_addr = driver_a.endpoint_addr();
+    driver_b.join(&[a_addr]);
+
+    let converged = pump_until_pair(
+        &mut driver_a,
+        &mut driver_b,
+        Duration::from_secs(5),
+        both_alive,
+    );
+
+    assert!(converged, "nodes did not converge within 5s");
+    assert_eq!(driver_a.snapshot().alive_count, 1);
+    assert_eq!(driver_b.snapshot().alive_count, 1);
+
+    driver_a.shutdown();
+    driver_b.shutdown();
+}
+
+/// Actor on node A sends InferenceRequest to actor on node B via the
+/// transport router + codec. InferenceResponse arrives back at node A.
+#[test]
+fn inference_request_roundtrips_across_two_nodes() {
+    let (mut driver_a, mut driver_b) = make_converged_pair();
+
+    let codecs = Arc::new(inference_codec_registry());
+
+    let mut rt_a = Runtime::new(RuntimeConfig::default());
+    let mut rt_b = Runtime::new(RuntimeConfig::default());
+
+    // Spawn echo actor on node B
+    let echo_addr = rt_b.spawn(EchoInferenceActor).unwrap();
+    rt_b.tick();
+
+    // Inbox on node A for responses
+    let response_inbox = rt_a.new_inbox::<InferenceResponse>().unwrap();
+    let inbox_addr = *response_inbox.addr();
+
+    // Build iroh-backed transports for actor messages
+    let transport_a_to_b = Arc::new(IrohActorTransport::new(
+        driver_a.endpoint().clone(),
+        driver_b.endpoint_addr(),
+        driver_a.tokio_handle(),
+    ));
+    let transport_b_to_a = Arc::new(IrohActorTransport::new(
+        driver_b.endpoint().clone(),
+        driver_a.endpoint_addr(),
+        driver_b.tokio_handle(),
+    ));
+
+    // Wire routes: A knows echo_addr is on B, B knows inbox_addr is on A
+    let router_a = TransportRouter::new();
+    router_a.add_route(echo_addr, transport_a_to_b);
+    let router_b = TransportRouter::new();
+    router_b.add_route(inbox_addr, transport_b_to_a);
+
+    rt_a.set_codec_registry(codecs.clone());
+    rt_a.set_transport_router(Arc::new(router_a));
+    rt_b.set_codec_registry(codecs.clone());
+    rt_b.set_transport_router(Arc::new(router_b));
+
+    // Send InferenceRequest from node A → actor on node B
+    rt_a.send_to(
+        echo_addr,
+        InferenceRequest {
+            prompt: "Hello from node A".into(),
+            max_tokens: 8,
+            temperature: 0.7,
+            reply_to: inbox_addr,
+        },
+    )
+    .unwrap();
+
+    // Allow iroh transport to deliver, then drain into runtime B
+    std::thread::sleep(Duration::from_millis(200));
+    drain_actor_messages(&driver_b, &codecs, &rt_b, Duration::from_millis(500));
+    rt_b.tick();
+
+    // Actor replied — allow transport to deliver, then drain into runtime A
+    std::thread::sleep(Duration::from_millis(200));
+    drain_actor_messages(&driver_a, &codecs, &rt_a, Duration::from_millis(500));
+
+    let response = response_inbox
+        .try_recv()
+        .expect("InferenceResponse should arrive at node A");
+    assert!(
+        response.text.contains("Hello from node A"),
+        "expected echo of prompt, got: {:?}",
+        response.text
+    );
+
+    driver_a.shutdown();
+    driver_b.shutdown();
+}
+
+/// When node B shuts down, node A detects the death via SWIM within the
+/// configured suspicion window.
+#[test]
+fn node_death_detected_via_swim_after_shutdown() {
+    let (mut driver_a, mut driver_b) = make_converged_pair();
+
+    assert_eq!(
+        driver_a.snapshot().alive_count, 1,
+        "precondition: A sees B alive"
+    );
+
+    // Kill node B
+    driver_b.shutdown();
+
+    // Pump node A until it sees zero alive peers
+    let start = Instant::now();
+    let timeout = Duration::from_secs(10);
+    let mut detected = false;
+    while start.elapsed() < timeout {
+        driver_a.recv();
+        driver_a.tick();
+        if driver_a.snapshot().alive_count == 0 {
+            detected = true;
+            break;
+        }
+        std::thread::sleep(Duration::from_millis(10));
+    }
+
+    assert!(
+        detected,
+        "node A should detect node B's death via SWIM within the suspicion window"
+    );
+
+    driver_a.shutdown();
+}
diff --git a/examples/single-gpu-inference/tests/t_codec.rs b/examples/single-gpu-inference/tests/t_codec.rs
new file mode 100644
index 0000000..a8e2e08
--- /dev/null
+++ b/examples/single-gpu-inference/tests/t_codec.rs
@@ -0,0 +1,153 @@
+//! T-codec: InferenceRequest / InferenceResponse serialization tests.
+//!
+//! Validates the message contract that all other smoke test groups depend on:
+//! - Roundtrip fidelity for both message types
+//! - Graceful error handling on corrupted input
+
+use single_gpu_inference::messages::{
+    inference_codec_registry, InferenceCodec, InferenceRequest, InferenceResponse,
+};
+use swactor::actor::ActorAddress;
+use swactor::transport::Codec;
+
+fn request_codec() -> &'static dyn Codec<InferenceRequest> {
+    &InferenceCodec
+}
+
+fn response_codec() -> &'static dyn Codec<InferenceResponse> {
+    &InferenceCodec
+}
+
+// ─── Roundtrip Tests ───────────────────────────────────────────────────────
+
+/// A request with representative field values survives encode → decode
+/// through both the direct codec and the type-erased registry path.
+#[test]
+fn inference_request_roundtrips_through_codec_and_registry() {
+    let original = InferenceRequest {
+        prompt: "Tell me about distributed systems".into(),
+        max_tokens: 128,
+        temperature: 0.7,
+        reply_to: ActorAddress::new_random(),
+    };
+
+    // Direct codec path
+    let codec = request_codec();
+    let bytes = codec.encode(&original).expect("encode should succeed");
+    let decoded = codec.decode(&bytes).expect("decode should succeed");
+    assert_eq!(decoded, original);
+
+    // Registry (type-erased) path — encode via TypeId, decode via type_tag
+    let registry = inference_codec_registry();
+    let (tag, payload) = registry
+        .encode(
+            std::any::TypeId::of::<InferenceRequest>(),
+            Box::new(original.clone()),
+        )
+        .expect("registry encode should succeed");
+    assert_eq!(tag, "smoke::InferenceRequest");
+
+    let any_msg = registry
+        .decode(&tag, &payload)
+        .expect("registry decode should succeed");
+    let decoded = any_msg
+        .downcast::<InferenceRequest>()
+        .expect("downcast should succeed");
+    assert_eq!(*decoded, original);
+}
+
+/// A response roundtrips through both the direct codec and the registry.
+#[test]
+fn inference_response_roundtrips_through_codec_and_registry() {
+    let original = InferenceResponse {
+        text: "Hello! I'd be happy to discuss distributed systems.".into(),
+    };
+
+    let codec = response_codec();
+    let bytes = codec.encode(&original).expect("encode should succeed");
+    let decoded = codec.decode(&bytes).expect("decode should succeed");
+    assert_eq!(decoded, original);
+
+    let registry = inference_codec_registry();
+    let (tag, payload) = registry
+        .encode(
+            std::any::TypeId::of::<InferenceResponse>(),
+            Box::new(original.clone()),
+        )
+        .expect("registry encode should succeed");
+    assert_eq!(tag, "smoke::InferenceResponse");
+
+    let any_msg = registry
+        .decode(&tag, &payload)
+        .expect("registry decode should succeed");
+    let decoded = any_msg
+        .downcast::<InferenceResponse>()
+        .expect("downcast should succeed");
+    assert_eq!(*decoded, original);
+}
+
+// ─── Corruption Tests ──────────────────────────────────────────────────────
+
+/// Completely random bytes are not valid JSON — the decoder must return
+/// an error rather than panicking or producing a garbage message.
+#[test]
+fn corrupted_bytes_produce_error_for_request() {
+    let codec = request_codec();
+    let garbage: Vec<u8> = vec![0xFF, 0x00, 0xDE, 0xAD, 0xBE, 0xEF];
+    let result = codec.decode(&garbage);
+    assert!(result.is_err(), "garbage bytes must produce an error");
+}
+
+#[test]
+fn corrupted_bytes_produce_error_for_response() {
+    let codec = response_codec();
+    let garbage: Vec<u8> = vec![0xFF, 0x00, 0xDE, 0xAD, 0xBE, 0xEF];
+    let result = codec.decode(&garbage);
+    assert!(result.is_err(), "garbage bytes must produce an error");
+}
+
+/// Truncated payload — valid JSON prefix cut short mid-value.
+#[test]
+fn truncated_request_bytes_produce_error() {
+    let codec = request_codec();
+    let original = InferenceRequest {
+        prompt: "hello".into(),
+        max_tokens: 64,
+        temperature: 0.5,
+        reply_to: ActorAddress::new_random(),
+    };
+    let bytes = codec.encode(&original).unwrap();
+    // Chop off the last half
+    let truncated = &bytes[..bytes.len() / 2];
+    let result = codec.decode(truncated);
+    assert!(result.is_err(), "truncated bytes must produce an error");
+}
+
+/// Empty input — zero bytes is not valid JSON.
+#[test]
+fn empty_bytes_produce_error() {
+    let req_codec = request_codec();
+    let res_codec = response_codec();
+    assert!(req_codec.decode(&[]).is_err());
+    assert!(res_codec.decode(&[]).is_err());
+}
+
+/// Valid JSON but wrong schema — a response payload fed to the request
+/// decoder. The missing required fields must cause an error.
+#[test]
+fn wrong_message_type_produces_error() {
+    let res_codec = response_codec();
+    let req_codec = request_codec();
+
+    let response = InferenceResponse {
+        text: "oops".into(),
+    };
+    let response_bytes = res_codec.encode(&response).unwrap();
+
+    // Decoding response bytes as a request should fail (missing prompt, max_tokens, etc.)
+    let result = req_codec.decode(&response_bytes);
+    assert!(
+        result.is_err(),
+        "decoding response bytes as request must fail"
+    );
+}
diff --git a/examples/single-gpu-inference/tests/t_integration.rs b/examples/single-gpu-inference/tests/t_integration.rs
new file mode 100644
index 0000000..c1cc9a7
--- /dev/null
+++ b/examples/single-gpu-inference/tests/t_integration.rs
@@ -0,0 +1,415 @@
+//! T-integration: End-to-end distributed inference tests.
+//!
+//! Two swactor nodes on localhost via iroh/QUIC. Node B runs an
+//! `InferenceActor` backed by a Python worker. Node A sends an
+//! `InferenceRequest` across the network, through the `RequestBridge`,
+//! into the actor, through the child process, and back.
+//!
+//! - `distributed_inference_through_echo_worker` — fast, uses canned echo responses.
+//! - `distributed_inference_through_tinygrad` — slow (#[ignore]), downloads a real
+//!   ~1B GGUF model and runs real tinygrad inference on CPU.
+
+use std::collections::HashMap;
+use std::sync::Arc;
+use std::time::{Duration, Instant};
+
+use distribution::iroh_driver::{IrohDriver, IrohDriverConfig};
+use distribution::node::DistributedNodeConfig;
+use distribution::registry::RegistryConfig;
+use distribution::swim::probe::SwimConfig;
+use iroh::{PublicKey, RelayMode};
+
+use swactor::runtime::{Runtime, RuntimeConfig};
+use swactor::transport::TransportRouter;
+
+use single_gpu_inference::inference_actor::{InferenceActor, InferenceActorStatus, RequestBridge};
+use single_gpu_inference::iroh_transport::{drain_actor_messages, IrohActorTransport, ACTOR_ALPN};
+use single_gpu_inference::messages::{inference_codec_registry, InferenceRequest, InferenceResponse};
+use swactor_process::{ProcessMode, ProcessSpec};
+
+// ── Iroh helpers (from t_cluster.rs) ────────────────────────────────────
+
+fn test_node_config() -> DistributedNodeConfig {
+    DistributedNodeConfig {
+        swim: SwimConfig {
+            probe_interval: 1,
+            probe_timeout: 3,
+            indirect_probes: 1,
+            suspicion_timeout: 5,
+            dead_reprobe_interval: 0,
+            ..SwimConfig::default()
+        },
+        cache_capacity: 100,
+        republish_interval: 50,
+        registry: RegistryConfig::default(),
+        metadata_lambda: 3,
+    }
+}
+
+fn make_driver() -> IrohDriver {
+    IrohDriver::new(IrohDriverConfig {
+        secret_key: None,
+        relay_mode: RelayMode::Disabled,
+        node: test_node_config(),
+        peer_auth: None,
+        additional_alpns: vec![ACTOR_ALPN.to_vec()],
+    })
+    .expect("failed to create iroh driver")
+}
+
+fn pump_one(driver: &mut IrohDriver) {
+    driver.recv();
+    driver.tick();
+}
+
+fn pump_until_pair(
+    a: &mut IrohDriver,
+    b: &mut IrohDriver,
+    timeout: Duration,
+    check_fn: fn(&IrohDriver, &IrohDriver) -> bool,
+) -> bool {
+    let start = Instant::now();
+    while start.elapsed() < timeout {
+        pump_one(a);
+        pump_one(b);
+        if check_fn(a, b) {
+            return true;
+        }
+        std::thread::sleep(Duration::from_millis(10));
+    }
+    false
+}
+
+fn sees_alive(driver: &IrohDriver, peer_key: &PublicKey) -> bool {
+    let snap = driver.snapshot();
+    let peer_hex: String = peer_key
+        .as_bytes()
+        .iter()
+        .map(|b| format!("{:02x}", b))
+        .collect();
+    snap.members
+        .iter()
+        .any(|m| m.node_id == peer_hex && m.state == "alive")
+}
+
+fn both_alive(a: &IrohDriver, b: &IrohDriver) -> bool {
+    let a_key = PublicKey::from_bytes(&a.node_id().0).unwrap();
+    let b_key = PublicKey::from_bytes(&b.node_id().0).unwrap();
+    sees_alive(a, &b_key) && sees_alive(b, &a_key)
+}
+
+fn make_converged_pair() -> (IrohDriver, IrohDriver) {
+    let mut driver_a = make_driver();
+    let mut driver_b = make_driver();
+
+    let a_addr = driver_a.endpoint_addr();
+    driver_b.join(&[a_addr]);
+
+    let converged = pump_until_pair(
+        &mut driver_a,
+        &mut driver_b,
+        Duration::from_secs(5),
+        both_alive,
+    );
+    assert!(converged, "cluster setup: nodes did not converge within 5s");
+
+    (driver_a, driver_b)
+}
+
+// ── Process helpers (from t_actor.rs) ────────────────────────────────────
+
+fn echo_worker_spec() -> ProcessSpec {
+    ProcessSpec {
+        command: "python3".into(),
+        args: vec![format!("{}/echo_worker.py", env!("CARGO_MANIFEST_DIR"))],
+        env: HashMap::new(),
+        working_dir: None,
+        mode: ProcessMode::Automated,
+        initial_pty_size: None,
+        kill_timeout: Some(Duration::from_secs(2)),
+        stdin_buffer_limit: None,
+    }
+}
+
+fn tinygrad_worker_spec() -> ProcessSpec {
+    let manifest = env!("CARGO_MANIFEST_DIR");
+    ProcessSpec {
+        command: format!("{manifest}/.venv/bin/python"),
+        args: vec![format!("{manifest}/tinygrad_worker.py")],
+        env: HashMap::new(),
+        working_dir: None,
+        mode: ProcessMode::Automated,
+        initial_pty_size: None,
+        kill_timeout: Some(Duration::from_secs(5)),
+        stdin_buffer_limit: None,
+    }
+}
+
+fn is_process_alive(pid: u32) -> bool {
+    std::fs::metadata(format!("/proc/{}", pid)).is_ok()
+}
+
+// ── Test ─────────────────────────────────────────────────────────────────
+
+#[test]
+fn distributed_inference_through_echo_worker() {
+    // 1. Converge two iroh drivers
+    let (mut driver_a, mut driver_b) = make_converged_pair();
+
+    let codecs = Arc::new(inference_codec_registry());
+
+    // 2. Create runtimes
+    let mut rt_a = Runtime::new(RuntimeConfig::default());
+    let mut rt_b = Runtime::new(RuntimeConfig::default());
+
+    // 3. On rt_b: spawn InferenceActor with echo_worker + RequestBridge
+    let status_inbox = rt_b.new_inbox::<InferenceActorStatus>().unwrap();
+    let sender = rt_b.create_sender();
+    let actor = InferenceActor::new(echo_worker_spec(), sender)
+        .with_status_addr(*status_inbox.addr());
+    let inference_addr = rt_b.spawn(actor).unwrap();
+
+    let bridge = RequestBridge { target: inference_addr };
+    let bridge_addr = rt_b.spawn(bridge).unwrap();
+
+    // 4. On rt_a: create response inbox
+    let response_inbox = rt_a.new_inbox::<InferenceResponse>().unwrap();
+    let inbox_addr = *response_inbox.addr();
+
+    // 5. Build iroh transports
+    let transport_a_to_b = Arc::new(IrohActorTransport::new(
+        driver_a.endpoint().clone(),
+        driver_b.endpoint_addr(),
+        driver_a.tokio_handle(),
+    ));
+    let transport_b_to_a = Arc::new(IrohActorTransport::new(
+        driver_b.endpoint().clone(),
+        driver_a.endpoint_addr(),
+        driver_b.tokio_handle(),
+    ));
+
+    // Wire routes: A sends to bridge_addr on B, B sends to inbox_addr on A
+    let router_a = TransportRouter::new();
+    router_a.add_route(bridge_addr, transport_a_to_b);
+    let router_b = TransportRouter::new();
+    router_b.add_route(inbox_addr, transport_b_to_a);
+
+    // 6. Install codecs and transport routers
+    rt_a.set_codec_registry(codecs.clone());
+    rt_a.set_transport_router(Arc::new(router_a));
+    rt_b.set_codec_registry(codecs.clone());
+    rt_b.set_transport_router(Arc::new(router_b));
+
+    // 7. Tick rt_b until InferenceActor reports WorkerReady
+    let start = Instant::now();
+    let mut worker_pid = None;
+    while start.elapsed() < Duration::from_secs(10) {
+        rt_b.tick();
+        if let Some(status) = status_inbox.try_recv() {
+            match status {
+                InferenceActorStatus::WorkerReady { pid } => {
+                    worker_pid = pid;
+                    break;
+                }
+                InferenceActorStatus::ProcessStarted => {}
+                other => panic!("unexpected status during startup: {:?}", other),
+            }
+        }
+        std::thread::sleep(Duration::from_millis(5));
+    }
+    let worker_pid = worker_pid.expect("echo_worker.py should report ready within 10s");
+
+    // 8. Send InferenceRequest from node A → bridge on node B
+    rt_a.send_to(
+        bridge_addr,
+        InferenceRequest {
+            prompt: "Hello from node A".into(),
+            max_tokens: 8,
+            temperature: 0.7,
+            reply_to: inbox_addr,
+        },
+    )
+    .unwrap();
+
+    // 9. Pump loop: drain messages on both sides, tick both runtimes
+    let start = Instant::now();
+    let mut got_response = false;
+    while start.elapsed() < Duration::from_secs(10) {
+        // Let transport deliver
+        std::thread::sleep(Duration::from_millis(100));
+
+        // Drain incoming actor messages on both sides
+        drain_actor_messages(&driver_b, &codecs, &rt_b, Duration::from_millis(100));
+        drain_actor_messages(&driver_a, &codecs, &rt_a, Duration::from_millis(100));
+
+        // Tick both runtimes
+        rt_b.tick();
+        rt_a.tick();
+
+        // Check for response
+        if let Some(response) = response_inbox.try_recv() {
+            // 10. Assert
+            assert!(
+                response.text.contains("Hello from node A"),
+                "expected echo of prompt, got: {:?}",
+                response.text
+            );
+            got_response = true;
+            break;
+        }
+    }
+    assert!(got_response, "should receive InferenceResponse within 10s");
+
+    // 11. Cleanup: stop inference actor, wait for process death, shutdown drivers
+    rt_b.stop_actor(inference_addr).unwrap();
+
+    let start = Instant::now();
+    while start.elapsed() < Duration::from_secs(5) {
+        rt_b.tick();
+        std::thread::sleep(Duration::from_millis(10));
+        if !is_process_alive(worker_pid) {
+            break;
+        }
+    }
+    assert!(
+        !is_process_alive(worker_pid),
+        "echo_worker.py (pid {}) should be dead after actor stop",
+        worker_pid
+    );
+
+    driver_a.shutdown();
+    driver_b.shutdown();
+}
+
+/// Full distributed inference through tinygrad with a real ~1B GGUF model.
+///
+/// Ignored by default — requires the `.venv` with tinygrad installed and
+/// downloads a ~1 GB model on first run. Run explicitly with:
+///   cargo test --package smoke-test distributed_inference -- --ignored
+#[test]
+#[ignore]
+fn distributed_inference_through_tinygrad() {
+    // 1. Converge two iroh drivers
+    let (mut driver_a, mut driver_b) = make_converged_pair();
+
+    let codecs = Arc::new(inference_codec_registry());
+
+    // 2. Create runtimes
+    let mut rt_a = Runtime::new(RuntimeConfig::default());
+    let mut rt_b = Runtime::new(RuntimeConfig::default());
+
+    // 3. On rt_b: spawn InferenceActor with tinygrad_worker + RequestBridge
+    let status_inbox = rt_b.new_inbox::<InferenceActorStatus>().unwrap();
+    let sender = rt_b.create_sender();
+    let actor = InferenceActor::new(tinygrad_worker_spec(), sender)
+        .with_status_addr(*status_inbox.addr());
+    let inference_addr = rt_b.spawn(actor).unwrap();
+
+    let bridge = RequestBridge { target: inference_addr };
+    let bridge_addr = rt_b.spawn(bridge).unwrap();
+
+    // 4. On rt_a: create response inbox
+    let response_inbox = rt_a.new_inbox::<InferenceResponse>().unwrap();
+    let inbox_addr = *response_inbox.addr();
+
+    // 5. Build iroh transports
+    let transport_a_to_b = Arc::new(IrohActorTransport::new(
+        driver_a.endpoint().clone(),
+        driver_b.endpoint_addr(),
+        driver_a.tokio_handle(),
+    ));
+    let transport_b_to_a = Arc::new(IrohActorTransport::new(
+        driver_b.endpoint().clone(),
+        driver_a.endpoint_addr(),
+        driver_b.tokio_handle(),
+    ));
+
+    let router_a = TransportRouter::new();
+    router_a.add_route(bridge_addr, transport_a_to_b);
+    let router_b = TransportRouter::new();
+    router_b.add_route(inbox_addr, transport_b_to_a);
+
+    // 6. Install codecs and transport routers
+    rt_a.set_codec_registry(codecs.clone());
+    rt_a.set_transport_router(Arc::new(router_a));
+    rt_b.set_codec_registry(codecs.clone());
+    rt_b.set_transport_router(Arc::new(router_b));
+
+    // 7. Wait for tinygrad model download + load (generous timeout)
+    let start = Instant::now();
+    let mut worker_pid = None;
+    while start.elapsed() < Duration::from_secs(600) {
+        rt_b.tick();
+        if let Some(status) = status_inbox.try_recv() {
+            match status {
+                InferenceActorStatus::WorkerReady { pid } => {
+                    worker_pid = pid;
+                    break;
+                }
+                InferenceActorStatus::ProcessStarted => {}
+                other => panic!("unexpected status during startup: {:?}", other),
+            }
+        }
+        std::thread::sleep(Duration::from_millis(50));
+    }
+    let worker_pid = worker_pid.expect("tinygrad_worker.py should report ready (model loaded)");
+
+    // 8. Send InferenceRequest from node A → bridge on node B
+    rt_a.send_to(
+        bridge_addr,
+        InferenceRequest {
+            prompt: "Say hello".into(),
+            max_tokens: 32,
+            temperature: 0.7,
+            reply_to: inbox_addr,
+        },
+    )
+    .unwrap();
+
+    // 9. Pump loop — generation on CPU can be slow
+    let start = Instant::now();
+    let mut got_response = false;
+    while start.elapsed() < Duration::from_secs(300) {
+        std::thread::sleep(Duration::from_millis(200));
+
+        drain_actor_messages(&driver_b, &codecs, &rt_b, Duration::from_millis(100));
+        drain_actor_messages(&driver_a, &codecs, &rt_a, Duration::from_millis(100));
+
+        rt_b.tick();
+        rt_a.tick();
+
+        if let Some(response) = response_inbox.try_recv() {
+            assert!(
+                !response.text.is_empty(),
+                "expected non-empty generated text from tinygrad, got empty string"
+            );
+            eprintln!("tinygrad response: {:?}", response.text);
+            got_response = true;
+            break;
+        }
+    }
+    assert!(
+        got_response,
+        "should receive InferenceResponse from tinygrad within timeout"
+    );
+
+    // 10. Cleanup
+    rt_b.stop_actor(inference_addr).unwrap();
+
+    let start = Instant::now();
+    while start.elapsed() < Duration::from_secs(10) {
+        rt_b.tick();
+        std::thread::sleep(Duration::from_millis(50));
+        if !is_process_alive(worker_pid) {
+            break;
+        }
+    }
+    assert!(
+        !is_process_alive(worker_pid),
+        "tinygrad_worker.py (pid {}) should be dead after actor stop",
+        worker_pid
+    );
+
+    driver_a.shutdown();
+    driver_b.shutdown();
+}
diff --git a/examples/single-gpu-inference/tests/t_vastai.rs b/examples/single-gpu-inference/tests/t_vastai.rs
new file mode 100644
index 0000000..33d9166
--- /dev/null
+++ b/examples/single-gpu-inference/tests/t_vastai.rs
@@ -0,0 +1,235 @@
+//! T-vastai: vast.ai API client tests with mocked HTTP responses.
+//!
+//! Validates the orchestration layer that rents and manages GPU instances.
+//! All tests hit a local wiremock server — no real API calls, no VAST_API_KEY needed.
+
+use reqwest::Client;
+use single_gpu_inference::vastai;
+use std::time::Duration;
+use wiremock::matchers::{header, method, path_regex};
+use wiremock::{Mock, MockServer, ResponseTemplate};
+
+// ─── find_offer ───────────────────────────────────────────────────────────
+
+/// Three offers at different prices — the client must pick the cheapest one.
+#[tokio::test]
+async fn find_offer_picks_cheapest_from_multiple_offers() {
+    let server = MockServer::start().await;
+
+    Mock::given(method("GET"))
+        .and(path_regex("/api/v0/bundles.*"))
+        .and(header("Authorization", "Bearer test-key"))
+        .respond_with(ResponseTemplate::new(200).set_body_json(serde_json::json!({
+            "offers": [
+                { "id": 101, "gpu_name": "RTX 3090", "dph_total": 0.30, "geolocation": "US" },
+                { "id": 102, "gpu_name": "RTX 3090", "dph_total": 0.15, "geolocation": "US" },
+                { "id": 103, "gpu_name": "RTX 3090", "dph_total": 0.25, "geolocation": "US" },
+            ]
+        })))
+        .mount(&server)
+        .await;
+
+    let client = Client::new();
+    let offer = vastai::find_offer(&client, &server.uri(), "test-key", "RTX 3090", &[])
+        .await
+        .expect("should find an offer");
+
+    assert_eq!(offer.id, 102, "must pick the cheapest offer (id=102, $0.15/hr)");
+    assert!((offer.dph_total - 0.15).abs() < f64::EPSILON);
+}
+
+/// Empty offer list — the client must return an error, not panic.
+#[tokio::test]
+async fn find_offer_returns_error_on_empty_list() {
+    let server = MockServer::start().await;
+
+    Mock::given(method("GET"))
+        .and(path_regex("/api/v0/bundles.*"))
+        .respond_with(ResponseTemplate::new(200).set_body_json(serde_json::json!({
+            "offers": []
+        })))
+        .mount(&server)
+        .await;
+
+    let client = Client::new();
+    let result = vastai::find_offer(&client, &server.uri(), "test-key", "RTX 3090", &[]).await;
+
+    assert!(result.is_err(), "empty offer list must produce an error");
+    assert!(
+        result.unwrap_err().contains("no offers"),
+        "error should mention no offers"
+    );
+}
+
+// ─── create_instance ──────────────────────────────────────────────────────
+
+/// Successful creation — parse the new_contract ID and verify SEED_ADDR is in the request.
+#[tokio::test]
+async fn create_instance_parses_contract_and_sends_seed_addr() {
+    let server = MockServer::start().await;
+
+    Mock::given(method("PUT"))
+        .and(path_regex("/api/v0/asks/102/"))
+        .and(header("Authorization", "Bearer test-key"))
+        .respond_with(ResponseTemplate::new(200).set_body_json(serde_json::json!({
+            "new_contract": 9999,
+            "success": true
+        })))
+        .expect(1)
+        .mount(&server)
+        .await;
+
+    let client = Client::new();
+    let info = vastai::create_instance(
+        &client,
+        &server.uri(),
+        "test-key",
+        102,
+        "iroh://node-abc123",
+        None,
+        "swactor-gpu:latest",
+    )
+    .await
+    .expect("create should succeed");
+
+    assert_eq!(info.contract_id, 9999);
+}
+
+/// Verify the request body contains SEED_ADDR by inspecting the recorded request.
+#[tokio::test]
+async fn create_instance_includes_seed_addr_in_env_payload() {
+    let server = MockServer::start().await;
+
+    Mock::given(method("PUT"))
+        .and(path_regex("/api/v0/asks/55/"))
+        .respond_with(ResponseTemplate::new(200).set_body_json(serde_json::json!({
+            "new_contract": 7777,
+        })))
+        .mount(&server)
+        .await;
+
+    let client = Client::new();
+    vastai::create_instance(
+        &client,
+        &server.uri(),
+        "test-key",
+        55,
+        "iroh://seed-address-xyz",
+        None,
+        "swactor-gpu:latest",
+    )
+    .await
+    .expect("create should succeed");
+
+    // Inspect the recorded request to verify the body contains SEED_ADDR.
+    let requests = server.received_requests().await.unwrap();
+    assert_eq!(requests.len(), 1);
+    let body: serde_json::Value = serde_json::from_slice(&requests[0].body).unwrap();
+    assert_eq!(
+        body["env"]["SEED_ADDR"],
+        "iroh://seed-address-xyz",
+        "request body must contain SEED_ADDR in the env payload"
+    );
+}
+
+// ─── wait_for_running ─────────────────────────────────────────────────────
+
+/// Polling sequence: loading → loading → running. Must extract IP and port.
+#[tokio::test]
+async fn wait_for_running_handles_loading_then_running_sequence() {
+    let server = MockServer::start().await;
+
+    // First two polls: loading
+    Mock::given(method("GET"))
+        .and(path_regex("/api/v0/instances/9999/"))
+        .respond_with(ResponseTemplate::new(200).set_body_json(serde_json::json!({
+            "instances": { "actual_status": "loading" }
+        })))
+        .up_to_n_times(2)
+        .expect(2)
+        .mount(&server)
+        .await;
+
+    // Third poll: running with IP and port
+    Mock::given(method("GET"))
+        .and(path_regex("/api/v0/instances/9999/"))
+        .respond_with(ResponseTemplate::new(200).set_body_json(serde_json::json!({
+            "instances": {
+                "actual_status": "running",
+                "public_ipaddr": "203.0.113.42",
+                "ssh_port": 31337
+            }
+        })))
+        .mount(&server)
+        .await;
+
+    let client = Client::new();
+    let running = vastai::wait_for_running(
+        &client,
+        &server.uri(),
+        "test-key",
+        9999,
+        Duration::from_millis(10), // fast polling for tests
+        10,
+    )
+    .await
+    .expect("should eventually reach running");
+
+    assert_eq!(running.ip, "203.0.113.42");
+    assert_eq!(running.port, 31337);
+}
+
+/// Terminal status (exited) — must return error immediately without further polling.
+#[tokio::test]
+async fn wait_for_running_returns_error_on_exited_status() {
+    let server = MockServer::start().await;
+
+    Mock::given(method("GET"))
+        .and(path_regex("/api/v0/instances/1234/"))
+        .respond_with(ResponseTemplate::new(200).set_body_json(serde_json::json!({
+            "instances": { "actual_status": "exited" }
+        })))
+        .expect(1) // Must only be called once — no further polling after terminal status.
+        .mount(&server)
+        .await;
+
+    let client = Client::new();
+    let result = vastai::wait_for_running(
+        &client,
+        &server.uri(),
+        "test-key",
+        1234,
+        Duration::from_millis(10),
+        10,
+    )
+    .await;
+
+    assert!(result.is_err(), "terminal status must produce an error");
+    assert!(
+        result.unwrap_err().contains("terminal status"),
+        "error should mention terminal status"
+    );
+}
+
+// ─── destroy_instance ─────────────────────────────────────────────────────
+
+/// Verify the correct DELETE request is sent for instance teardown.
+#[tokio::test]
+async fn destroy_instance_sends_correct_delete_request() {
+    let server = MockServer::start().await;
+
+    Mock::given(method("DELETE"))
+        .and(path_regex("/api/v0/instances/9999/"))
+        .and(header("Authorization", "Bearer test-key"))
+        .respond_with(ResponseTemplate::new(200).set_body_json(serde_json::json!({
+            "success": true
+        })))
+        .expect(1)
+        .mount(&server)
+        .await;
+
+    let client = Client::new();
+    vastai::destroy_instance(&client, &server.uri(), "test-key", 9999)
+        .await
+        .expect("destroy should succeed");
+}
diff --git a/examples/single-gpu-inference/tests/test_worker.py b/examples/single-gpu-inference/tests/test_worker.py
new file mode 100644
index 0000000..4f5c13c
--- /dev/null
+++ b/examples/single-gpu-inference/tests/test_worker.py
@@ -0,0 +1,154 @@
+"""T-worker: tinygrad worker protocol tests.
+
+Spawns tinygrad_worker.py --stub as a subprocess and verifies the
+stdin/stdout JSON protocol defined in SPEC.md §2.3.
+"""
+
+import json
+import os
+import signal
+import subprocess
+import sys
+import time
+
+import pytest
+
+WORKER_SCRIPT = os.path.join(os.path.dirname(__file__), "..", "tinygrad_worker.py")
+PYTHON = sys.executable
+
+
+def spawn_worker():
+    """Spawn the worker in --stub mode and return the Popen handle."""
+    proc = subprocess.Popen(
+        [PYTHON, WORKER_SCRIPT, "--stub"],
+        stdin=subprocess.PIPE,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.PIPE,
+        text=True,
+    )
+    return proc
+
+
+def read_line(proc, timeout=5):
+    """Read one line from the worker's stdout, with a timeout."""
+    import selectors
+    sel = selectors.DefaultSelector()
+    sel.register(proc.stdout, selectors.EVENT_READ)
+    events = sel.select(timeout=timeout)
+    if not events:
+        raise TimeoutError(f"No output from worker within {timeout}s")
+    line = proc.stdout.readline()
+    sel.close()
+    if not line:
+        raise EOFError("Worker closed stdout")
+    return json.loads(line.strip())
+
+
+def send_line(proc, obj):
+    """Write a JSON line to the worker's stdin."""
+    proc.stdin.write(json.dumps(obj) + "\n")
+    proc.stdin.flush()
+
+
+def send_raw(proc, text):
+    """Write raw text to the worker's stdin."""
+    proc.stdin.write(text + "\n")
+    proc.stdin.flush()
+
+
+class TestWorkerStartup:
+    """Worker prints {"status": "ready"} on startup."""
+
+    def test_worker_emits_ready_on_startup(self):
+        proc = spawn_worker()
+        try:
+            msg = read_line(proc)
+            assert msg == {"status": "ready"}, f"Expected ready status, got: {msg}"
+        finally:
+            proc.terminate()
+            proc.wait(timeout=5)
+
+
+class TestWorkerInference:
+    """Valid request produces a valid response with non-empty text."""
+
+    def test_valid_request_returns_non_empty_response(self):
+        proc = spawn_worker()
+        try:
+            ready = read_line(proc)
+            assert ready["status"] == "ready"
+
+            send_line(proc, {"prompt": "Say hello", "max_tokens": 8})
+            response = read_line(proc)
+
+            assert "response" in response, f"Response missing 'response' field: {response}"
+            assert isinstance(response["response"], str)
+            assert len(response["response"]) > 0, "Response text must be non-empty"
+        finally:
+            proc.terminate()
+            proc.wait(timeout=5)
+
+    def test_multiple_requests_in_sequence(self):
+        """Worker handles multiple requests without restarting."""
+        proc = spawn_worker()
+        try:
+            read_line(proc)  # ready
+
+            for prompt in ["Hello", "World", "Test"]:
+                send_line(proc, {"prompt": prompt, "max_tokens": 8})
+                response = read_line(proc)
+                assert "response" in response
+                assert len(response["response"]) > 0
+        finally:
+            proc.terminate()
+            proc.wait(timeout=5)
+
+
+class TestWorkerMalformedInput:
+    """Malformed JSON produces {"error": "..."} and worker continues."""
+
+    def test_malformed_json_returns_error_and_continues(self):
+        proc = spawn_worker()
+        try:
+            read_line(proc)  # ready
+
+            # Send garbage
+            send_raw(proc, "this is not json {{{")
+            err = read_line(proc)
+            assert "error" in err, f"Expected error response, got: {err}"
+
+            # Worker should still be alive and handle the next valid request
+            send_line(proc, {"prompt": "Still alive?", "max_tokens": 8})
+            response = read_line(proc)
+            assert "response" in response, "Worker should continue after malformed input"
+            assert len(response["response"]) > 0
+        finally:
+            proc.terminate()
+            proc.wait(timeout=5)
+
+    def test_partial_json_returns_error(self):
+        proc = spawn_worker()
+        try:
+            read_line(proc)  # ready
+
+            send_raw(proc, '{"prompt": "incomplete')
+            err = read_line(proc)
+            assert "error" in err
+        finally:
+            proc.terminate()
+            proc.wait(timeout=5)
+
+
+class TestWorkerEOFShutdown:
+    """Closing stdin (EOF) causes worker to exit cleanly with code 0."""
+
+    def test_eof_causes_clean_exit(self):
+        proc = spawn_worker()
+        read_line(proc)  # ready
+
+        # Close stdin
+        proc.stdin.close()
+
+        # Worker should exit cleanly
+        exit_code = proc.wait(timeout=5)
+        assert exit_code == 0, f"Worker exited with code {exit_code}, expected 0"
diff --git a/examples/single-gpu-inference/tinygrad_worker.py b/examples/single-gpu-inference/tinygrad_worker.py
new file mode 100644
index 0000000..722f981
--- /dev/null
+++ b/examples/single-gpu-inference/tinygrad_worker.py
@@ -0,0 +1,159 @@
+#!/usr/bin/env python3
+"""tinygrad compute worker — stdin/stdout JSON protocol.
+
+Startup: loads a model (or uses --stub for testing), prints {"status": "ready"}.
+
+Protocol (newline-delimited JSON):
+  → stdin:  {"prompt": "Say hello", "max_tokens": 64, "temperature": 0.7}
+  ← stdout: {"response": "Hello! How can I help you today?"}
+
+Errors:
+  ← stdout: {"error": "description of what went wrong"}
+
+Flags:
+  --stub         Skip model loading; return a canned response for every request.
+                 Used for component tests that exercise the protocol without a GPU.
+  --model NAME   Model from tinygrad's built-in catalog (default: llama3.2:1b).
+"""
+
+import argparse
+import json
+import os
+import sys
+
+
+def main():
+    parser = argparse.ArgumentParser(description="tinygrad inference worker")
+    parser.add_argument("--stub", action="store_true",
+                        help="Stub mode: skip model loading, return canned responses")
+    parser.add_argument("--model", default="llama3.2:1b",
+                        help="Model name from tinygrad catalog (default: llama3.2:1b)")
+    args = parser.parse_args()
+
+    if args.stub:
+        model_data = None
+    else:
+        try:
+            model_data = _load_model(args.model)
+        except Exception as e:
+            import traceback
+            _log(traceback.format_exc())
+            _write({"error": f"model load failed: {e}"})
+            sys.exit(1)
+
+    # Signal readiness
+    _write({"status": "ready", "pid": os.getpid()})
+
+    # Request loop
+    for line in sys.stdin:
+        line = line.strip()
+        if not line:
+            continue
+        try:
+            request = json.loads(line)
+        except (json.JSONDecodeError, ValueError) as e:
+            _write({"error": f"invalid JSON: {e}"})
+            continue
+
+        if "prompt" not in request:
+            _write({"error": "missing 'prompt' field"})
+            continue
+
+        prompt = request["prompt"]
+        max_tokens = request.get("max_tokens", 64)
+        temperature = request.get("temperature", 0.7)
+
+        try:
+            text = _generate(model_data, prompt, max_tokens, temperature, stub=args.stub)
+            _write({"response": text})
+        except Exception as e:
+            _write({"error": f"generation failed: {e}"})
+
+
+def _write(obj):
+    """Write a JSON object as a single line to stdout and flush."""
+    print(json.dumps(obj), flush=True)
+
+
+def _log(msg):
+    """Write a log message to stderr (not part of the JSON protocol)."""
+    print(msg, file=sys.stderr, flush=True)
+
+
+def _load_model(model_name):
+    """Load a GGUF model via tinygrad 0.12.0's built-in catalog."""
+    from tinygrad import Tensor
+    from tinygrad.helpers import fetch
+    from tinygrad.apps.llm import Transformer, SimpleTokenizer, models
+
+    if model_name not in models:
+        available = ", ".join(models.keys())
+        raise ValueError(f"Unknown model '{model_name}'. Available: {available}")
+
+    url = models[model_name]
+    _log(f"Downloading {model_name} from {url}...")
+    gguf_path = fetch(url)
+
+    _log(f"Loading model from {gguf_path}...")
+    model, kv = Transformer.from_gguf(Tensor(gguf_path), max_context=512)
+    tokenizer = SimpleTokenizer.from_gguf_kv(kv)
+
+    # Find stop token IDs for generation
+    tokens_list = kv.get("tokenizer.ggml.tokens", [])
+    stop_ids = set()
+    for i, tok in enumerate(tokens_list):
+        if tok in ("<|end_of_text|>", "<|eot_id|>", "</s>", "<|endoftext|>"):
+            stop_ids.add(i)
+
+    # Find EOS token ID for chat template end-of-turn
+    eot_id = None
+    for i, tok in enumerate(tokens_list):
+        if tok == "<|eot_id|>":
+            eot_id = i
+            break
+    if eot_id is None:
+        for i, tok in enumerate(tokens_list):
+            if tok in ("</s>", "<|end_of_text|>"):
+                eot_id = i
+                break
+
+    _log(f"Model loaded. Stop IDs: {stop_ids}, EOT ID: {eot_id}")
+    return {"model": model, "tokenizer": tokenizer, "stop_ids": stop_ids, "eot_id": eot_id}
+
+
+def _format_chat_tokens(tokenizer, prompt, eot_id):
+    """Format a prompt using Llama 3 instruct chat template."""
+    tokens = tokenizer.role("user")
+    tokens += tokenizer.encode(prompt)
+    if eot_id is not None:
+        tokens += tokenizer.end_turn(eot_id)
+    tokens += tokenizer.role("assistant")
+    return tokens
+
+
+def _generate(model_data, prompt, max_tokens, temperature, stub=False):
+    """Generate text from a prompt."""
+    if stub:
+        return f"stub response to: {prompt}"
+
+    model = model_data["model"]
+    tokenizer = model_data["tokenizer"]
+    stop_ids = model_data["stop_ids"]
+    eot_id = model_data["eot_id"]
+
+    # Use chat template for instruction-tuned models
+    tokens = _format_chat_tokens(tokenizer, prompt, eot_id)
+    prompt_len = len(tokens)
+
+    for i, tok_id in enumerate(model.generate(tokens)):
+        if tok_id in stop_ids:
+            tokens.pop()  # remove the stop token from output
+            break
+        if i + 1 >= max_tokens:
+            break
+
+    return tokenizer.decode(tokens[prompt_len:])
+
+
+if __name__ == "__main__":
+    main()