Third slice of the per-device CUDA context-ownership refactor planned at ~/.claude/plans/plan-the-per-device-worker-abstract-micali.md. The leader's `NcclState`, every `Comm::all_reduce` issued by the TP layers, the leader-side KV cache reset, and the TP forward step itself now all run on the per-device worker thread — the same OS thread that bound the leader's `CudaContext` at startup. What this phase changes: - `Job` gains `NcclInit`, `NcclSanity`, `CloneLeaderComm` (Phase 3 bridge — Phase 4 removes), `TransferInTp`, `DropTp`, `TpClearKv`, `TpForwardLogits`. Plus a new `TpHandle(u64)` opaque key. - `DeviceWorkerState` gains `nccl: NcclState` and `tp_models: HashMap<TpHandle, Box<TpLeaderModel>>` (+ counter). - `WorkerPool` loses its `leader_nccl` field; gains a `leader_worker: Arc<DeviceWorkerHandle>` passed at construction. `init_nccl`, `nccl_sanity_check`, `load_dense_shard`, `generate_step`, `clear_kv_cache` all route their leader-side ops through `Job::Nccl*` / `Job::Tp*` instead of spawn_blocking against a Mutex-wrapped state. `generate_step` returns `Vec<f32>` instead of a device-resident `Tensor` — the worker copies logits to CPU before reply so the async caller can sample on a CPU candle tensor with zero device-context touch. - `TpLoadedModel.leader_model: Arc<Mutex<TpLeaderModel>>` → opaque `leader_handle: TpHandle`. The boxed `TpLeaderModel` lives in the worker thread's slab; both the model's CUDA tensors and the embedded `Arc<Comm>` clones release on the same thread that allocated them (the Drop semantics constraint cudarc forces). - `Job::CloneLeaderComm` is a Phase 3 bridge: the TP shard load still runs in spawn_blocking and needs the leader's `Arc<Comm>` to build the row-parallel layers' AllReduce ops. The Job clones the Comm out of the worker's NcclState and ships it back as `SendComm`. Phase 4 deletes this bridge when the load itself moves onto the worker. - `Job::NcclInit` and `Job::NcclSanity` are ungated by `cuda` so the no-cuda `NcclState` stubs (which reply with `cuda_feature_not_enabled`) still flow through the same channel uniformly; the cuda-only TP variants (CloneLeaderComm, Transfer/Drop/Clear/Forward Tp) remain gated. What this phase doesn't touch (yet): - TP shard load itself — still spawn_blocking, bridged via `CloneLeaderComm`. Phase 4 moves it to `Job::TpLoadShard` and reads `state.nccl.comm()` directly inside the worker. - Single-GPU model loads — still spawn_blocking, transferred via `Job::TransferIn`. Phase 4 moves them. - `device_vram_mb` / `cuda_mem_mb` / `log_construction_complete` helpers — still present, used inside spawn_blocking load closures. Phase 4 cleanup folds them into `dispatch.rs`. `tp/mod.rs::WorkerPool::spawn` gained a required `leader_worker: Arc<DeviceWorkerHandle>` argument. Three external callers were updated: `CandleHarness::load_tp` (passes the cached device worker), `main.rs::tp_smoke` (spawns a fresh worker), and the two `tp_worker_lifecycle*.rs` integration tests. Public API unchanged. fmt + clippy clean; 37 lib tests + all integration tests pass. CUDA-only TP integration smoke deferred to the next deploy on beast. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
cortex
A Rust reverse-proxy and fleet management layer for multi-node GPU inference
clusters. Cortex sits in front of one or more neuron daemons (each running
candle-based inference on a local GPU host) and presents a unified OpenAI +
Anthropic compatible API surface.
Problem
Running local LLMs across multiple GPU nodes (different VRAM tiers, different model affinities) requires a unified API surface that:
- Presents a single
/v1/modelscatalogue merging every model that can be served by any neuron in the fleet. - Routes requests to the correct node based on where a model is loaded (or can be loaded), handling cold-load and eviction transparently.
- Manages model lifecycle — load on demand, unload cold models, pin
critical ones — by calling each neuron's
/models/{load,unload}API. - Translates between OpenAI and Anthropic request/response envelopes so every client speaks whichever dialect it prefers.
- Captures per-request metrics (tokens, tok/s, TTFT, latency) and exposes them as Prometheus counters/histograms.
Architecture
┌──────────────┐ ┌──────────┐ ┌────────────┐ ┌────────────┐
│ Claude Code │ │ Zed/IDE │ │ Tidal / mm │ │ curl / etc │
└──────┬───────┘ └─────┬────┘ └──────┬─────┘ └──────┬─────┘
│ │ │ │
└────────────────┴──────┬───────┴───────────────┘
│
┌──────────▼──────────┐
│ cortex │
│ (cortex-gateway) │
│ │
│ Router · Metrics │
│ Evictor · Translate│
└──┬──────┬────────┬──┘
│ │ │
┌──────────▼┐ ┌──▼─────┐ ┌▼──────────┐
│ neuron │ │ neuron │ │ neuron │
│ :13131 │ │ :13131 │ │ :13131 │
│ candle │ │ candle │ │ candle │
└───────────┘ └────────┘ └───────────┘
private network (.internal)
Crates
| Crate | Purpose |
|---|---|
cortex-core |
Shared types: config, node/model state, metrics, OpenAI/Anthropic envelopes, harness trait, discovery types |
cortex-gateway |
Axum HTTP server: proxy, router, evictor, poller, metrics exporter |
neuron |
Per-node daemon: GPU discovery, in-process candle inference, model lifecycle API |
cortex-cli |
CLI entrypoint (cortex serve, cortex status, etc.) |
Node setup
Each GPU node runs neuron (listening on :13131). Neuron uses
huggingface/candle for in-process inference — there is no external
inference subprocess to manage.
The neuron RPM (helexa-neuron) ships a systemd unit:
dnf copr enable helexa/helexa
dnf install helexa-neuron
systemctl enable --now neuron
Gateway config
# /etc/cortex/cortex.toml
[gateway]
listen = "0.0.0.0:31313"
metrics_listen = "0.0.0.0:31314"
[eviction]
strategy = "lru" # lru | priority
defrag_after_cycles = 50
[[neurons]]
name = "beast"
endpoint = "http://beast.internal:13131"
[[neurons]]
name = "benjy"
endpoint = "http://benjy.internal:13131"
Model placement profiles live in models.toml — see models.example.toml.
Building
cargo build --release
CI
Every push triggers format, lint, and test checks. Ensure these pass locally before pushing:
cargo fmt --check --all # must be clean
cargo clippy --workspace -- -D warnings # warnings are errors
cargo test --workspace # all tests must pass
Tagged releases (v*) additionally build SRPMs for both cortex and
helexa-neuron and publish to COPR.
Running
# start the gateway
cortex serve --config /etc/cortex/cortex.toml
# check fleet status
cortex status
# list all models across nodes
curl http://localhost:31313/v1/models
License
GPL-3.0