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githubhelexa-ai/helexa

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helexa

Near-frontier AI for mortals.

helexa is a self-hosted LLM serving stack, written in Rust, for people who run open-weight models on their own consumer GPUs. It has two components:

  • cortex — the per-operator control plane and LLM proxy. It sits in front of your GPU fleet and presents a unified OpenAI + Anthropic compatible API surface, handling model routing, lifecycle management (load / unload / evict), request translation, and metrics.
  • neuron — the per-host LLM harness. One instance runs on every GPU host, serving candle-based in-process inference and managing local hardware discovery and model lifecycle.

Why

Two principles constrain everything in this repository:

  1. Frontier or close to it. helexa serves the open-weight models that get nearest to frontier capability — not every architecture ever published.
  2. Consumer hardware. Everything must run on the cards mortals can actually buy: a 3060 here, a 4090 there, a 5090 if you got lucky. Mixed VRAM tiers across mismatched boxes are the expected topology, not a degraded case.

GPU acquisition is harder than it was a year ago, and the gap between what cloud providers charge and what your own silicon costs keeps widening. The intersection of those two principles — near-frontier models, squeezed onto hardware you own — is helexa's entire niche.

The secondary objective is predictable consumption. If you own the hardware, your tooling shouldn't break because a cloud provider changed billing, deprecated a model, or reshaped an API. cortex's OpenAI and Anthropic surfaces are a stability contract: point your editor, agent, or CLI at it once, and it keeps working.

What helexa is not

This is an intentionally different path from vLLM, SGLang, and peers — not a smaller version of them. Out of scope, permanently:

  • Any-model breadth. Architectures are ported because they're at or near the frontier, not to complete a compatibility matrix.
  • Datacenter-class scheduling. No sophisticated continuous-batching / paged-attention machinery — the workload is a handful of operators and their agents, not 200 QPS.
  • Wrapping external inference engines. neuron builds directly on candle; every model architecture it serves is implemented in this repository, ported against the HuggingFace reference.

One thing that is not a principle: CUDA exclusivity. All high-end consumer hardware is in scope. helexa is CUDA-only today because that's the hardware on the bench — nothing ships untested — and ROCm or other consumer accelerators join as soon as there's real hardware to build against.

In scope, and where the engineering effort goes: aggressive quantization (GGUF Q4_K_M / Q6_K / Q8_0), NCCL tensor parallelism across heterogeneous consumer GPUs, careful CUDA failure handling, and single-request latency — the performance that one operator at a keyboard actually feels.

Architecture

┌──────────────┐  ┌──────────┐  ┌────────────┐  ┌────────────┐
│ Claude Code  │  │ Zed/IDE  │  │ Tidal / mm │  │ curl / etc │
└──────┬───────┘  └─────┬────┘  └──────┬─────┘  └──────┬─────┘
       │                │              │               │
       └────────────────┴──────┬───────┴───────────────┘
                               │  OpenAI + Anthropic APIs
                    ┌──────────▼──────────┐
                    │      cortex         │
                    │  (cortex-gateway)   │
                    │                     │
                    │  Router · Metrics   │
                    │  Evictor · Translate│
                    └──┬──────┬────────┬──┘
                       │      │        │
            ┌──────────▼┐  ┌──▼─────┐  ┌▼──────────┐
            │  neuron   │  │ neuron │  │  neuron   │
            │  :13131   │  │ :13131 │  │  :13131   │
            │  candle   │  │ candle │  │  candle   │
            └───────────┘  └────────┘  └───────────┘
                  private network (.internal)

cortex discovers each neuron's hardware (devices, VRAM, compute capability) at runtime and matches it against a model catalogue (models.toml) to decide placement: which models fit where, what to evict when VRAM is tight, where to route a request right now. Adding a GPU host to the fleet is one [[neurons]] entry — no device specs in config.

Crates

CratePurpose
cortex-coreShared types: config, node/model state, metrics, OpenAI/Anthropic envelopes, harness trait, discovery types
cortex-gatewayAxum HTTP server: proxy, router, evictor, poller, metrics exporter
neuronPer-host daemon: GPU discovery, in-process candle inference, NCCL tensor parallelism, model lifecycle API
cortex-cliCLI entrypoint (cortex serve, cortex status, etc.)
helexa-acpAgent Client Protocol bridge — connects ACP editors (Zed, etc.) to any OpenAI-compatible endpoint, cortex by default

The engine

neuron runs inference in-process on candle — there is no external inference server to babysit. The parts that earn their keep:

  • Per-device worker threads. Every CUDA device gets one dedicated OS thread that owns its CUDA context for the daemon's lifetime. All loads, forward passes, KV-cache resets, NCCL collectives, VRAM queries, and unloads route through it; tensors never escape it alive. Context binding is pinned to a known thread, the CUDA Drop contract is structurally safe, and a driver error poisons one worker — visibly — instead of hanging the whole process.
  • Tensor parallelism on consumer cards. Megatron-style row/column parallel layers with NCCL all-reduce, spanning the mismatched GPUs you actually have. A step watchdog aborts wedged collectives instead of letting a request hang forever.
  • Current model focus: the Qwen3 family — dense and GGUF-quantized, including the hybrid linear-attention (Gated DeltaNet) generation. Vision support is in progress. Each architecture is ported against its HuggingFace reference implementation.

See CLAUDE.md for design rationale and crates/neuron/src/harness/device_worker/ for the worker narrative.

Install

Pre-built RPMs for Fedora:

dnf copr enable helexa/helexa
dnf install cortex            # on the gateway host
dnf install helexa-neuron     # on each GPU host
systemctl enable --now cortex   # or neuron, respectively

Configure

# /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 (VRAM requirements, quant, device minimums, pinning) live in models.toml — see models.example.toml.

Run

# start the gateway
cortex serve --config /etc/cortex/cortex.toml

# check fleet status
cortex status

# one catalogue across every node
curl http://localhost:31313/v1/models

Build from source

cargo build --release

CI runs on every push; keep it green locally:

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*) build SRPMs for cortex and helexa-neuron and publish to COPR.

Status

Pre-1.0 and moving fast. The gateway path (routing, eviction, translation, metrics) is stable and tested; the candle-native engine is under active development — expect the supported-model list to track the open-weight frontier, deliberately narrowly.

Development happens at https://git.lair.cafe/helexa/helexa; https://github.com/helexa-ai/helexa is a read-only mirror.

License

GPL-3.0

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