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feat(neuron,cortex-core): source-aware loader (scheme:org/name)
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>
2026-06-01 13:42:11 +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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