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>
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