rob thijssen 9b8bd146f6
All checks were successful
CI / Format (push) Successful in 36s
build-prerelease / Resolve version stamps (push) Successful in 38s
CI / Clippy (push) Successful in 2m19s
CI / Test (push) Successful in 4m32s
CI / Build cortex SRPM (push) Has been skipped
CI / Publish cortex to COPR (push) Has been skipped
build-prerelease / Build neuron-blackwell (push) Successful in 3m43s
CI / Build neuron SRPM (push) Has been skipped
CI / Publish neuron to COPR (push) Has been skipped
CI / Bump version in source (push) Has been skipped
build-prerelease / Build cortex binary (push) Successful in 4m16s
build-prerelease / Package cortex RPM (push) Successful in 1m23s
build-prerelease / Build neuron-ampere (push) Successful in 4m56s
build-prerelease / Build neuron-ada (push) Successful in 5m1s
build-prerelease / Package helexa-neuron-ampere RPM (push) Successful in 2m51s
build-prerelease / Package helexa-neuron-ada RPM (push) Successful in 3m0s
build-prerelease / Package helexa-neuron-blackwell RPM (push) Successful in 3m39s
build-prerelease / Publish to rpm.lair.cafe (unstable) (push) Successful in 59s
feat(tp): --tp-smoke CLI subcommand + remote validation script
Adds a one-shot diagnostic that exercises the lower half of the TP
stack — WorkerPool::spawn, init_nccl, nccl_sanity_check — in isolation
from model load and inference. Runs N-1 worker subprocesses (rank 0
stays in this process), joins them in an NCCL communicator on the
specified CUDA devices, all_reduces a sentinel 1u32 per rank, verifies
the observed_sum equals world_size on every rank, then shuts down.

Output is `status=ok` on stdout (plus key=value lines for tp_size and
cuda_devices) when every check passes, non-zero exit + tracing on
stderr otherwise. The smoke command is diagnostic-only and not exposed
through the daemon HTTP API.

script/tp-smoke.sh wraps it with an ssh invocation against a fleet
host (default beast — the only host with 2 GPUs) and asserts the
status line, mirroring the validate-neuron.sh ergonomics.

This is step 1 of the TP test plan. A failure here means TP cannot
work on the host at all; step 2 (Stage 7b-iv) wires real model load
and inference through the same WorkerPool primitives.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-19 19:40:25 +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.

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

Description
No description provided
Readme GPL-3.0 1.9 MiB
Languages
Rust 90.6%
Cuda 4.6%
Shell 3.9%
Python 0.9%