mlx-beam

B.E.A.M. — Batched Engine for Apple Metal. Light and modular inference engine, built on MLX.

Work in progress. See the status table below and the changelog.

Install

uv tool install mlx-beam
beam doctor

Inside a uv project: uv add mlx-beam, then uv run beam doctor.

beam doctor reports the Python, MLX, device and memory it sees (--json for scripts) and exits non-zero when MLX is missing or fails to load.

Serve

beam serve --model p4ik/Qwen3.8-27B-MLX-OptiQ-5bit --port 8000 \
  --max-completion-tokens 4096 --min-response-tokens 512 --max-reasoning-tokens 8192 \
  --kv-bits 8

That is an OpenAI-compatible server (/v1/chat/completions, /v1/completions, /v1/responses, /v1/models) plus /health, which reports what was actually built: the KV layout per layer, the batching and cache settings, the Metal allocator's memory counters (active, peak, cache — RSS does not see these), and every request default with where it came from (flag, the model's generation_config.json, or mlx-lm's own). POST /health/reset-peak starts a fresh peak window for a measurement.

Flags follow mlx-lm's names where mlx-lm has one (--temp, --top-p, --kv-bits, --prompt-cache-size, --chat-template, …). Token limits say what they count: --max-context (prompt plus generated, a hard cap), --max-prompt-tokens (prompt, a hard cap), --max-completion-tokens (generated, the default a request may override), --max-reasoning-tokens (the think block; closed by force at the budget) and --min-response-tokens (what the answer keeps after the block). beam serve --help lists them all with their units.

Requests may use the names other servers taught clients: max_tokens, thinking_token_budget, reasoning: {effort, max_tokens}, enable_thinking, reasoning_effort. The model's thinking is returned in reasoning (--reasoning-field switches to reasoning_content, both, or none), counted in usage.completion_tokens_details.reasoning_tokens, and flagged there when a limit cut it (thinking_truncated, response_truncated).

What sets it apart

• Robust prefix cache — RAM and SSD tiers, checkpoints for hybrid models. Survives model swaps and restarts.
• Expert streaming — Mixture-of-experts models larger than memory. Residency configurable, from minimal RAM to fully resident.
• No bloat — The core is the token path. Vision, audio, conversion, structured output and tool-call repair are optional extras.
• Batched MTP — Multi-token prediction stays on with many requests at once.
• Batched vision — Images go through the same scheduler; no request waits behind a picture.
• No stalls — A short request beside a long prefill answers in seconds.
• Mixed-precision KV cache — Bits per layer, set at conversion. Quantized after the prefill by default (the prefill never reads quantized data); --kv-prefill quantized writes it quantized from the first token for profiles measured that way.
• Thinking budget — A hard cap on the reasoning trace, per request.
• Responses API — Next to chat completions, stateless.

The engine reads standard MLX checkpoints and the B.E.A.M. package layout (extras/ next to the shards; see the model cards under huggingface.co/p4ik).

Why it exists

Existing MLX servers either stop at the basics or grow things that have no place in an inference engine: a built-in game, a cloud path that arrives with an update. The ones we ran daily also had bugs where it matters most: prefix cache, batching under load, vision. B.E.A.M. keeps the core to the token path and fixes those paths at the source. Everything else is an extra you choose to install; nothing ever ships in the core that you did not ask for.

Status

Measured numbers are published as they are measured, with machine, model and date.

Contributing

See CONTRIBUTING.md. Rules for coding agents are in AGENTS.md.

License

Apache-2.0. Vendored components keep their own licenses; see NOTICE.