Phase 1 of plan-source-aware-loader-preflight. Makes neuron's
loader treat `huggingface:org/name` and `helexa:org/name` as
first-class distinct sources with per-source endpoint + cache,
while staying backwards-compatible with bare `org/name` ids.
Zero behavior change for existing operator configs.
Motivation: helexa is adding an EU-hosted registry
(`registry.helexa.ai`) alongside HF. Both speak HF-compatible
wire format, but the bytes, jurisdiction, trust root, and cache
namespace are distinct. The loader needs to disambiguate which
registry serves a given model id, and to keep their caches from
colliding on disk when both happen to host the same `org/name`.
What lands:
- `cortex-core::source` — new module. `ModelSourceId { scheme,
org, name }` with `FromStr` accepting both `scheme:org/name`
and bare `org/name`. `Display` round-trips. `repo_path()`
emits the `org/name` half for the hf-hub `Api::model(...)`
call regardless of which scheme/endpoint we're hitting.
Rejects malformed input with typed `ParseError` variants
(empty scheme, missing slash, scheme with `/`, name with
`:`, etc.).
- `neuron::config::CandleHarnessConfig` gains
`default_source: Option<String>` and
`sources: HashMap<String, SourceConfig>`. `SourceConfig`
mirrors what `hf_hub::ApiBuilder` consumes: endpoint URL,
optional `auth_env` (env var name read at startup so secrets
stay out of TOML), and optional cache_dir. Defaults
synthesise a `huggingface` entry pointing at
`https://huggingface.co` with the legacy `hf_cache` field as
its cache_dir — so existing configs that only set `hf_cache`
keep working unchanged.
- `CandleHarness::new(bind_url, &CandleHarnessConfig)` replaces
`CandleHarness::new(bind_url, hf_cache)`. Resolves every
configured source's auth env var and cache dir up front so
`hf_api_for(scheme)` is a pure HashMap lookup on the hot
load path. Only the `huggingface` scheme gets the legacy
`HF_HUB_CACHE`/`HF_HOME` env-var fallback chain; other
schemes resolve to whatever the operator typed.
- `hf_api()` -> `hf_api_for(scheme)`. Builds an
`hf_hub::Api` with the source's endpoint, cache_dir, and
auth token. Errors with a useful message naming the
configured schemes when an unknown scheme is requested.
- `CandleHarness::load_model` parses `spec.model_id` into a
`ModelSourceId`, substitutes `default_source` for bare ids,
and threads the parsed source through `preflight`,
`resolve_files`, `resolve_dense_files`, `load_arch_gguf`,
`load_arch_dense`, and `load_tp`. The hf-hub `Api::model()`
call now uses `source_id.repo_path()` so registry calls hit
the right URL shape regardless of scheme.
- `preflight()` signature gains a `&ModelSourceId` parameter
(it's the canonical id for log lines and error display);
`RepoFetchFailed.model_id` etc. now carry the
scheme-qualified form so operator-visible errors echo
exactly what was configured.
- `neuron.example.toml` documents the new
`[harness.candle.sources.*]` table with commented-out
examples for `huggingface` (explicit override) and `helexa`.
Tests:
- 13 new unit tests in `cortex-core::source` covering parse /
display round-trip, default-scheme substitution semantics,
and every `ParseError` variant.
- 6 new unit tests in `neuron::config` covering the
`effective_sources` synth (legacy `hf_cache` carry-through,
explicit override preservation, helexa-alongside-huggingface)
and `effective_default_source` fallback.
- 2 new unit tests in `harness::candle::tests` covering
multi-scheme `hf_api_for` routing, including the
"unknown scheme" error path naming configured schemes.
- Preflight integration tests updated to construct
`ModelSourceId` and assert against the scheme-qualified
error form.
CI gate: cargo fmt --check, cargo clippy --workspace
--all-targets -- -D warnings, cargo test --workspace (all 24
test groups ok, zero failures).
Out of scope (Phase 3):
- Cortex catalogue `source` field — independent of Phase 1+2,
ships when the registry comes online.
- `helexa` source endpoint itself — separate project; this
PR adds the client-side rails only.
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