rob thijssen 7c19da9361
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feat(neuron): construction-complete vram/config dump + logits health + per-step vram
Three additive diagnostics that turn the 2026-05-27 q5k Qwen3.6-27B
incident from "guess at KV cache / quant sizes" into "read the
journal":

1. Construction-complete summary in TpQwen3_5ForCausalLM::load and
   TpQwen3ForCausalLM::load. After the last "after layer N" log fires,
   each rank emits a single info line with: free_mb/total_mb (the
   number that drops by ~9 GB between per-layer and first-request on
   beast, with no inference traffic), every resolved config knob
   (vocab_size, hidden_size, num_layers, head_dim, num_kv_heads,
   max_position_embeddings), and a per-token KV-cache byte estimate.
   For Qwen3-Next also includes the linear/full-attention layer split
   so the hybrid architecture's cache cost is unambiguous.

2. Logits health snapshot on sample failure. Today the failure logs
   "A weight is negative, too large or not a valid number" with no
   context — was it a NaN cascade, an Inf, a negative weight?
   `logits_health(&logits)` computes nan/pos_inf/neg_inf/neg counts
   plus finite_min/max/mean on the failure path (zero cost on the
   success path) and emits a warn line just before the wrapper's
   terminal "failed, model marked poisoned" log. Wired into both the
   prefill and decode sample sites of the non-streaming AND streaming
   TP chat paths.

3. VRAM snapshot at prefill complete + every decode step. The
   "prefill complete" info line now carries vram_free_mb so the
   activations + KV growth from the prefill itself is visible. The
   per-step trace line gets vram_free_mb too, so an operator running
   with RUST_LOG=trace can watch headroom shrink token by token.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-27 09:04:55 +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
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