Replaces the per-token Rust delta-rule loop in
`arch/qwen3_5/linear_attn.rs::GatedDeltaNet::forward` with a single
dispatch to the `gated_delta_rule_recurrence` kernel imported from
mistralrs in 1ebbe87.
The kernel is V-tiled with compile-time BK (one block per (V-tile,
batch*head), one thread per V-column, BK state floats in registers).
For Qwen3.6's per-rank `(B=1, H=24, D_k=128, D_v=128)` shape this
collapses ~6 candle tensor-op launches per token per layer (each
~50µs CUDA dispatch overhead, so ~300µs/token/layer × 48 linear-
attention layers = 14ms in launch overhead alone) to a single
kernel launch with full ILP / register residency.
New free function `run_delta_rule`:
- cuda branch (when q is on a CUDA device): flattens
`(B, H, ...)` → `(BH, ...)`, dispatches the kernel via
`crate::cuda::gdn::gated_delta_rule_recurrence_cuda`, reshapes
outputs back to `(B, H, L, D_v)` and state to `(B, H, D_k, D_v)`.
- cpu fallback: the original per-token Rust loop, unchanged. Keeps
cargo test --workspace passing on hosts without cuda.
Dispatch decision lives in the wrapper (`q.device().is_cuda()`).
Build: `cargo build -p neuron --features cuda` compiles + links;
clippy clean on both CPU and cuda paths. 32 lib tests still pass
(none of them exercise this code path on cuda; smoke test for the
TP variant is the deployed Tbilisi probe).
Stage 8d-3 wires the conv1d kernels; 8d-4 the chunked prefill;
8d-5 the same wiring for `tp/tp_qwen3_5.rs`.
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