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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.
  • Text-to-image. Z-Image-Turbo (6B S3-DiT, Apache 2.0) served candle-native through the same device-worker discipline: OpenAI /v1/images/generations end to end, ~11 s for a 1024x1024 image on an RTX 4090, metered in megapixel-steps.
  • 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, which neurons a model may run on, and what it may displace when one runs out of VRAM — live in models.toml. models.example.toml is the field reference; placement & displacement explains how the two fit together, and is worth reading before you set residency_priority on anything.

Full documentation — using helexa and operating it — is at helexa.ai/docs; the source lives under helexa.ai/content/docs/.

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

Tailoring model behaviour

System prompts are application-owned. Send yours through the standard field for whichever API you speak, and the serving chain — edge → router → cortex → neuron — passes it to the model verbatim.

# OpenAI chat completions
curl http://localhost:31313/v1/chat/completions \
  -H 'content-type: application/json' \
  -d '{"model": "helexa/balanced",
       "messages": [{"role": "system", "content": "Reply only in French."},
                    {"role": "user",   "content": "Good morning"}]}'

# OpenAI responses — the `instructions` field is the system slot
curl http://localhost:31313/v1/responses \
  -H 'content-type: application/json' \
  -d '{"model": "helexa/balanced",
       "instructions": "Reply only in French.",
       "input": "Good morning"}'

# Anthropic messages — top-level `system`, string or content-block array
curl http://localhost:31313/v1/messages \
  -H 'content-type: application/json' \
  -d '{"model": "helexa/balanced", "max_tokens": 256,
       "system": "Reply only in French.",
       "messages": [{"role": "user", "content": "Good morning"}]}'

The passthrough guarantee

helexa will never, to any request you proxy through it:

  • inject a system prompt, house style or preamble of its own,
  • rewrite, truncate or reorder the one you sent,
  • supply a default when you send none.

If you send no system prompt, the model receives none. Behaviour you did not ask for is a bug — please report it.

Streaming and non-streaming behave identically, and the guarantee holds on every surface above. Two details worth knowing:

  • Several system messages are all forwarded, unmerged and in order. The model sees the last one last, so in practice the last instruction wins. Send one message if you want certainty.
  • /no_think in a prompt is a Qwen-family convention the model interprets to skip its reasoning block. It is the model's feature, not ours — helexa neither adds nor strips it. Note it currently suppresses reasoning on /v1/chat/completions but not on /v1/responses, where a reasoning model may think regardless (#223); with a small max_output_tokens the whole budget can go on reasoning and the reply comes back empty with status: "incomplete". Give Responses requests room (a few hundred tokens) when the model reasons.

Why it works this way

The ecosystem serves an unenumerable diversity of workloads through OpenAI- and Anthropic-compatible APIs. An operator cannot curate prompts for use cases they will never see, and a proxy that quietly edits your payload makes model behaviour impossible to reason about. So the split is: applications own the prompt, operators own the fleet.

If you specifically want centrally-managed prompts across your own workloads, run your own helexa mesh — it is open source, and that is a different deployment from the shared helexa.ai ecosystem.

Operators: this is a contract, not a default you may flip. cortex and helexa-router proxy inference bodies without adding to them; nothing in the chain is a place to put prompt content.

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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