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feat(catalogue,gateway): model aliases (helexa/small, helexa/balanced, helexa/large)
Operators can now define tier aliases in models.toml:

  [aliases]
  "helexa/small" = "Qwen/Qwen3-1.7B"
  "helexa/balanced" = "Qwen/Qwen3-8B"
  "helexa/large" = "Qwen/Qwen3.6-27B"

A client request for `model: "helexa/small"` is resolved to the concrete
model id at routing time. The gateway also rewrites the proxied body's
`model` field to the concrete id so neuron sees a name that matches its
loaded handle (otherwise the harness rejects the request).

Motivated by the finger-in-the-wind benchmark: same "what's the capital
of Georgia" probe runs in 2.5s on the 1.7B vs 6.7s on the 27B with
identical correctness. Aliases let clients pick a latency tier without
hardcoding model ids, and let operators swap targets without changing
client code.

Changes:
  * cortex-core: `ModelCatalogue` gains `aliases: HashMap<String, String>`
    + `resolve_alias(&str) -> &str`. Unit tests cover the basic
    resolution + TOML round-trip.
  * cortex-gateway:
    * `RouteDecision` gains `resolved_model_id: String`. `router::resolve`
      consumes aliases at entry and threads the concrete id through.
    * Handlers (chat_completions, completions, anthropic_messages
      streaming + non-streaming) rewrite the body's `model` field with
      `rewrite_model_in_body` before proxying, using the resolved id
      for metrics labels, LRU touch, and the body itself.
    * `/v1/models` (Pass 4) emits each alias as its own entry mirroring
      the target's `loaded` flag, feasible_on, and locations — clients
      browsing the endpoint see both names and can pick either.
  * `models.toml` declares the three tier aliases; `models.example.toml`
    documents the section as opt-in.
  * Integration tests verify: end-to-end alias→concrete request flow,
    alias surfacing in /v1/models, and no-op fall-through for
    non-alias model ids.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-26 16:10:41 +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

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