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fix(neuron): chunked TP-vision prefill + pre-flight VRAM guard
agent-0 sent a ~13k-token prompt + image; the TP vision prefill was
single-shot, so it tried to materialise activations for all 12,960
positions at once and OOM'd rank 1 mid-forward. Rank 1 died before
issuing its row-parallel AllReduce, stranding rank 0 on the collective
(it hung holding the pool lock). The text path survives the same size
because it chunks the prefill.

Chunk the vision prefill the same way:

- TpQwen3_5ForCausalLM::prefill_with_images_chunked encodes the image(s)
  once, then walks the pre-expanded prompt in prefill_chunk_tokens()
  windows, splicing the patch-embedding rows into whichever chunk(s)
  carry <|image_pad|> positions (pure-text chunks take the plain
  forward). Activation is bounded by the chunk, not the prompt.
- Every rank runs the identical chunk sequence (chunk_size threaded
  through GenerateStepWithImages / TpForwardLogitsWithImages /
  generate_step_with_images), so the per-chunk AllReduces stay paired
  across ranks with no extra sync — the KV cache accumulates via the
  growing offset, only the last chunk's logits are kept.

Pre-flight guard (validate_vision_prefill): even chunked, a long
prompt's KV cache can exhaust VRAM mid-forward, and on TP that hangs
the collective. Reject up front with a clean InsufficientVram when the
estimated footprint exceeds free VRAM, so a doomed request fails fast
instead of hanging the daemon. Heuristic + tunable
(NEURON_VISION_PREFILL_MB_PER_1K_TOKENS / _BASE_MB); default permissive
so the now-working 12,960-token case still passes. Applied to every
vision path (single-GPU + TP); single-GPU vision stays single-shot for
now, so the guard is its protection until it's chunked too.

Tests: pre-flight guard behaviour; RPC round-trip carries chunk_size.
The chunked forward is cuda-gated — CI CUDA type-check validates it.

Refs #16 / TP-vision. Operational note: a TP rank OOM still hangs the
daemon (needs restart); making a worker failure abort the leader's
collective is separate, broader TP hardening.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-04 17:21:36 +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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