rob thijssen 957f704efa
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feat(neuron): OpenAI Responses API + ci cuda-check runner label
Step 2 of the Responses rollout: native `/v1/responses` endpoint on
neuron that consumes the same InferenceEvent stream as
`/v1/chat/completions` but emits it as the Responses API's named
SSE event family. No gateway-side translation.

## Surface

- `cortex-core::responses` envelope types: `ResponsesRequest`,
  `ResponsesInput` (text | items), `ResponsesInputItem` (message |
  function_call | function_call_output | reasoning),
  `ResponsesContentPart` (input_text | input_image | output_text),
  `ResponsesResponse`, `ResponsesOutputItem`, `ResponsesUsage`. Plus
  a `events::*` constant module so the projector and the wire shape
  stay in sync without string-typos.

- `neuron::wire::openai_responses`:
  - `request_to_chat(req)` flattens Responses input + instructions
    into a `ChatCompletionRequest` the candle harness already
    understands. Text-only Parts collapse to a string; mixed
    text+image Parts go to chat's content-array shape; reasoning
    items drop; function_call / function_call_output round-trip
    via tool_calls / tool_call_id metadata so the surface is
    consistent for the day the harness emits tool calls.
  - `project_responses_stream(rx, meta)` reads InferenceEvents
    and emits the eight named events that compose a Responses
    stream: response.created → output_item.added → content_part.added
    → output_text.delta×N → output_text.done → content_part.done
    → output_item.done → response.completed. Synthesises start
    frames if the producer skips Start (poisoned model, early
    disconnect) so the stream stays coherent.
  - `build_response(meta, text, reason, usage)` for the
    non-streaming path.

- `CandleHarness::inference_stream(req)` extracted from
  `chat_completion_stream`, returning a typed `InferenceStream`
  (event receiver + id/created/model_id metadata). Both
  `chat_completion_stream` and the new `responses_stream` are now
  thin wrappers that pick their wire projection. TP path got the
  same treatment (`chat_completion_tp_stream` → `inference_tp_stream`).

- `POST /v1/responses` route on neuron. Non-streaming returns one
  buffered `ResponsesResponse`; streaming returns axum SSE with
  both event names and JSON data per frame (Responses, unlike
  chat completions, uses named `event:` lines). Reused
  `inference_error_response` helper hoisted out so the chat and
  responses handlers share the InferenceError → HTTP mapping.

## CI

Also bundles the `cuda-check` runner-label fix from feedback on
commit 1859777: `runs-on: rpm` doesn't ship the CUDA toolkit so
cudarc's nvcc-version build script blew up. Switched to
`runs-on: cuda-13.0` per the existing labels.

## Scope cuts (documented in the modules)

- `previous_response_id` rejected at translate time with 400
  (`code: chained_conversation_not_supported`) — stateful chained
  conversations need a persistence layer we haven't built.
- Reasoning items dropped (no Qwen3 `<think>` routing yet).
- Single output item per response (one `"message"` carrying text);
  `function_call` items reserved but not synthesised.
- Streaming events cover the core set; `response.in_progress`
  and the web_search / image_generation event families are
  out-of-scope.

22 new tests: 5 in cortex-core (envelope round-trips), 13 in
neuron::wire (request translator + projector + non-streaming
builder), 4 in neuron's tests/api.rs (route surface — 503 when no
candle, 400 on previous_response_id, 404 on missing model for
both stream and non-stream).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-31 11:13:44 +03:00

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/models catalogue 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.

Inside the daemon, every CUDA device gets one dedicated OS thread (named cuda-dev-N) that owns the device's CUDA context for the daemon's lifetime. Model loads, forward passes, KV-cache resets, NCCL collectives, VRAM queries, and unloads all route through that thread via a job channel; tensors never escape it alive. This pins context binding to a known thread, makes the CUDA Drop contract structurally safe, and isolates driver-error poisoning to one worker rather than the whole process. See CLAUDE.md for the design rationale and crates/neuron/src/harness/device_worker/ for the code.

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

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