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>
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.
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