Beast testing surfaced a real regression in the dynamic-resolution default: a tall 808×1600 image resized (within the 1024² max_pixels) to a 90×44 patch grid = 3960 patches, exceeding the vision tower's hard `num_position_embeddings = 2304` pos-embed budget. The per-rank `patch count 3960 exceeds pos_embed budget 2304` error fired mid-TP- forward and poisoned the device context, bricking the model until reload. Hard-cap `max_pixels` to `2304 × 16² = 589_824` px (≤ 2304 patches → ≤ 576 LM tokens), clamping even the operator env override. `smart_resize` floors the pixel count under the cap, so no resized image can ever exceed the budget — the tower check never fires, no poison. The pos-embed grid (48×48) is the resolution Qwen3.6 was trained at, so the cap is principled, not just defensive. Still ~3× the old fixed 196 tokens, and the book-cover OCR test (1176 patches) already reads full title+subtitle. Test: a huge/tall/wide/extreme image battery stays within the 2304 patch budget. (Per-rank-error poison robustness itself remains issue #17.) Co-Authored-By: Claude Opus 4.8 (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.
Inside the daemon, every CUDA device gets one dedicated OS thread
(named cuda-dev-N) that owns the device's CUDA context for the
daemon's lifetime. Model loads, forward passes, KV-cache resets,
NCCL collectives, VRAM queries, and unloads all route through that
thread via a job channel; tensors never escape it alive. This pins
context binding to a known thread, makes the CUDA Drop contract
structurally safe, and isolates driver-error poisoning to one worker
rather than the whole process. See CLAUDE.md for the design
rationale and crates/neuron/src/harness/device_worker/ for the code.
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