Phase 3 of plan-source-aware-loader-preflight. Adds an optional `source` field to `ModelProfile` and threads it through the router's cold-load path so a profile pointing at the helexa registry forwards `helexa:<id>` to neuron's `/models/load` instead of leaving neuron to substitute its `default_source` (typically `huggingface`). Without this, an operator who declares `source = "helexa"` in models.toml would still see neuron fetch from HuggingFace — the catalogue → ModelSpec translation in `profile_to_spec` was dropping the scheme on the floor. What lands: - `cortex-core::catalogue::ModelProfile.source: Option<String>`. None is the default and preserves pre-Phase-3 behaviour. - `cortex-gateway::router::qualified_model_id(profile)` — small pure helper, extracted from `profile_to_spec` so it can be unit-tested. Empty-string `source` is treated as None so operators who blank out a previously-set value don't trip a scheme-with-no-scheme failure mode in neuron. - `models.example.toml` documents the new field with a commented-out helexa-scheme example pointing back at neuron.example.toml's matching sources block. Tests: - 2 new unit tests in `cortex-core::catalogue`: source-absent round-trip and source-present round-trip through TOML. - 3 new unit tests in `cortex-gateway::router`: pass-through when None, prefix when Some, pass-through on empty-string source. - ModelProfile literal in catalogue's existing test updated to carry `source: None`. CI gate: cargo fmt --check, cargo clippy --workspace --all-targets -- -D warnings, cargo test --workspace (24 test groups ok, zero failures). Completes Phase 3. With Phases 1+2+3 landed: - neuron parses `scheme:org/name`, routes per-source hf-hub Api with disambiguated cache. - preflight returns structured errors before any device allocation. - cortex catalogue declares per-model source jurisdiction and forwards it to neuron. The registry itself (registry.helexa.ai service, MinIO, nginx, mirror fabric) is the next moving piece — landing under a separate project per the design discussion. Co-Authored-By: Claude Opus 4.7 <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.
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