rob thijssen 1ebbe87651
Some checks failed
build-prerelease / Build cortex binary (push) Blocked by required conditions
CI / Test (push) Waiting to run
build-prerelease / Resolve version stamps (push) Successful in 29s
CI / Format (push) Successful in 40s
CI / Clippy (push) Successful in 2m23s
build-prerelease / Build neuron-blackwell (push) Has started running
build-prerelease / Build neuron-ampere (push) Has been cancelled
build-prerelease / Build neuron-ada (push) Has been cancelled
build-prerelease / Package cortex RPM (push) Has been cancelled
build-prerelease / Package helexa-neuron-ada RPM (push) Has been cancelled
build-prerelease / Package helexa-neuron-ampere RPM (push) Has been cancelled
build-prerelease / Package helexa-neuron-blackwell RPM (push) Has been cancelled
build-prerelease / Publish to rpm.lair.cafe (unstable) (push) Has been cancelled
CI / Build cortex SRPM (push) Has been cancelled
CI / Build neuron SRPM (push) Has been cancelled
CI / Publish cortex to COPR (push) Has been cancelled
CI / Publish neuron to COPR (push) Has been cancelled
CI / Bump version in source (push) Has been cancelled
feat(stage-8d-1): import mistralrs GDN CUDA kernels — build infra only
Stage 8d (new): port the Gated DeltaNet CUDA kernels from
EricLBuehler/mistral.rs to close the ~500x decode performance gap
we measured on Qwen3.6-27B TP-2 (~12s/token in our pure-candle path
vs ~37 T/s in mistralrs on the same hardware).

This commit lays the build infrastructure with zero behavioural
change. Subsequent commits (8d-2 .. 8d-5) wire each kernel into the
qwen3_5 architecture and TP variant.

Added:
- `crates/neuron/build.rs` — uses `cudaforge::KernelBuilder` to compile
  every `src/cuda/*.cu` file into `libneuroncuda.a` under the `cuda`
  feature, then links it + `cudart`. Mirrors mistralrs's
  `mistralrs-core/build.rs` setup verbatim (same NVCC flag set, same
  sm_<80 bf16 gate).
- `crates/neuron/src/cuda/gdn.cu` — five kernels ported verbatim from
  upstream:
    * `gated_delta_rule_recurrence` (V-tiled per-token decode)
    * `chunked_gated_delta_rule_recurrence` (BT=64 chunked prefill)
    * `causal_conv1d_update` (single-token conv decode)
    * `causal_conv1d_full` (multi-token conv prefill)
    * `fused_gdn_gating` (beta = sigmoid(b); g = -exp(A_log) *
      softplus(a + dt_bias))
- `crates/neuron/src/cuda/gdn.rs` — Rust wrappers around the kernels,
  cudarc::CudaSlice::device_ptr boilerplate identical to upstream.
- `crates/neuron/src/cuda/ffi.rs` — `extern "C"` decls (subset of
  upstream's ffi.rs covering only the five GDN kernels; MoE / SSM /
  top-k decls land here when we absorb those too).
- `crates/neuron/src/cuda/mod.rs` — re-exports + module docs.

Cargo wiring: `cudaforge` added as an optional build-dep, activated
by the `cuda` feature. CPU build is unchanged (the `cuda/` module is
fully `#[cfg(feature = "cuda")]`). The cuda feature build inside the
patched container compiles `gdn.cu` (1 of 1 kernels) and links
clean.

Licensing: upstream files preserve their MIT origin via per-file
comment banners pointing to the mistralrs path. No behaviour-relevant
edits to the .cu kernels — local diff against upstream is just the
banner. The `.rs` wrappers and `ffi.rs` subset are also from upstream;
their structure (module path `crate::cuda::ffi::*`) matches identically
so future kernel imports drop in unchanged.

CPU clippy + 32 lib tests pass; `cargo clippy --features cuda` clean
inside the runner container.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-21 11:34:11 +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.

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

Description
No description provided
Readme GPL-3.0 1.9 MiB
Languages
Rust 90.6%
Cuda 4.6%
Shell 3.9%
Python 0.9%