The TP-2 inference probe against Qwen3.6-27B surfaced:
worker rank 1 ClearKvCache: expected KvCacheCleared, got
GenerateStepOk
Caused by pipe poisoning. The previous shape of `generate_step`:
for w in workers { w.send_only(GenerateStep) } // 1. fan-out
let logits = spawn_blocking(leader.forward)??; // 2. early return on err
for w in workers { w.recv_only() } // 3. drain (skipped on 2's err)
When step 2 returned `Err` (e.g. a dtype mismatch we hadn't seen
before, an OOM, a downstream squeeze that didn't match the shape),
the function bailed before step 3 — but workers had already written
`GenerateStepOk` to their stdout pipes, since their forwards (and
the NCCL collectives inside) completed independently of the leader's
post-collective Rust-side work.
The next call (typically `ClearKvCache` at the start of the *next*
inference request) would then send a fresh request and read those
stale replies as if they were the new operation's. Once a pipe is
poisoned, every subsequent call surfaces the same shape of error
even though nothing's actually broken.
Fix: introduce two helpers in `tp/mod.rs`:
- `drain_workers(workers, check)` — reads exactly one response from
every worker regardless of individual outcomes. Returns
`Vec<String>` of `rank N: detail` strings for any non-OK reply.
- `combine_leader_workers(leader, worker_errs, op)` — folds the
leader's `Result<Result<T>>` (the spawn_blocking shape) with the
worker drain into a single `Result<T>`. Leader failure takes
precedence but worker errors get appended so both halves surface.
`generate_step` and `clear_kv_cache` now use this pattern. Worst case:
both halves fail and the operator sees a combined error message;
either way the pipes are always drained so the next call's recv
matches the request it sent.
Note: the model is still poisoned in the current state — the
operator needs to either `POST /models/unload` + reload, or
`systemctl restart neuron`, to recover. The fix prevents *future*
desync; it doesn't repair existing stale pipe state.
Stage 7c-ii crash detection was tracked as the canonical solution to
this class of issue; this is the minimum-viable subset.
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