Splash

A local inference engine for Apple silicon, built around the model.

Splash serves a small set of models to coding agents and to any OpenAI or Anthropic compatible client, on one Mac. On a 48 GB M5 Pro it decodes Qwen3.8-27B at 2× the speed of the next-fastest engine and, with a 32K context cached, returns the first token in 282 ms. Its kernels, draft model, and memory plan are specialized for each model it serves. That is why it is fast, and why there is nothing to configure.

Quick start

Apple M3 or newer, macOS 26.4 or later, Homebrew, and 36 GB of unified memory (48 GB or more recommended).

brew install incoai/tap/splash
splash serve --model incoai/Qwen3.8-27B-Splash

The first run downloads and verifies the model package, checks available memory, and starts serving on 127.0.0.1:8000.

Once it prints Ready, leave this terminal open. Open http://127.0.0.1:8000 in your browser, or run an installed coding agent from another terminal:

splash opencode    # or: splash claude / splash codex / splash hermes

Press Ctrl+C in the server terminal to stop Splash.

Use the API

Splash speaks OpenAI Chat Completions (/v1/chat/completions), OpenAI Responses (/v1/responses), and Anthropic Messages (/v1/messages), all with streaming, tool calls, JSON Schema output, images, and inline PDFs. /tokenize and /apply-template return token IDs and the rendered prompt without running the model.

curl http://127.0.0.1:8000/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "incoai/Qwen3.8-27B-Splash",
    "messages": [{"role": "user", "content": "Explain speculative decoding in one sentence."}]
  }'

model is optional. If you set it, it must match the package you served. Reasoning is on by default. "reasoning_effort": "none" turns it off, and Qwen3.8-27B also takes low, medium, and xhigh.

Models

Package (--model)	Contents	Download
incoai/Qwen3.8-27B-Splash	Qwen3.8-27B, 4-bit, with its DFlash 2 draft	17.4 GB
incoai/Qwen3.6-35B-A3B-Splash	Qwen3.6-35B-A3B, 4-bit, with its DFlash 2 draft	20.9 GB

--model takes any owner/repo that holds a Splash package, a format DEVELOPMENT.md describes. Plain MLX or Transformers checkpoints do not work. Private repositories need HF_TOKEN. Packages download into the Hugging Face cache, and brew upgrade splash keeps them, along with model links and agent sessions.

To download new models to another disk, set HF_HUB_CACHE before the first run:

HF_HUB_CACHE=/Volumes/Models/huggingface splash serve --model incoai/Qwen3.8-27B-Splash

Model links and agent sessions stay in Splash's data directory. Existing models are not moved.

Settings

There is no config file. The server binds 127.0.0.1:8000 by default. Context supports up to the model’s native 256K window; usable capacity depends on available memory.

splash serve accepts these optional flags:

• --port: local HTTP port. Defaults to SPLASH_PORT or 8000.
• --max-memory: ceiling on Metal allocations, e.g. 28G. Default: auto.
• --max-context: context limit, up to 256K, e.g. 100K. Default: auto.
• --max-image-pixels: maximum resized pixels per image. Default: 4,194,304.
• --allowed-host: extra HTTP Host name to accept, for a proxy. Repeatable.
• --api-key: require this key on API requests, as a bearer token or x-api-key. Defaults to SPLASH_API_KEY.
• --no-webui: turn off the chat page.

Set SPLASH_PORT in both the server and agent shells to use another port. Separate ports allow separate servers; their memory limits are independent.

If the model does not fit in the memory available, startup prints a memory budget breakdown and stops.

Authentication is off by default. Set SPLASH_API_KEY in the shell that runs splash serve and in the shell that runs an agent, and both sides use it. Health and readiness probes stay public.

• Experimental cache offloading: PR #3 adds SSD offloading for KV cache and GDN states. Build from that branch and set --max-cache-disk 8G to enable it. This helps preserve reusable prefixes when RAM is limited, reducing repeated prefill.

Performance

Measured on an M5 Pro (16-core GPU, 48 GB): selected SPEED-Bench coding prompts over HTTP, a 1,024-token output limit, reasoning on (medium for the 27B). The ratio in each cell is against the next-fastest engine we measured.

Metric	Qwen3.6-35B-A3B	Qwen3.8-27B
Decode · short prompt	210 tok/s (1.7×)	74 tok/s (2.0×)
Prefill · 32K prompt	2,011 tok/s (1.3×)	363 tok/s (1.2×)
Cached time to first token · 32K replay	123 ms (6.6×)	282 ms (7.3×)
Aggregate decode · 4 concurrent short prompts	357 tok/s (2.0×)	170 tok/s (3.9×)

Splash led on every measure at every prompt length we tested, and the lead grows with load: 3.8× at four concurrent 32K requests on the 35B. The launch post has the method and the full comparison against oMLX, Lily, uzu, and Ollama.

Design

The runtime, scheduler, cache, and API are shared. Everything else is rebuilt per model:

• A draft trained for the model. Speculative decoding is the decode path in Splash, not an option. Every model ships with its own DFlash 2 draft, and one pass of the target verifies a block of tokens in parallel.
• Kernels compiled for exact shapes. Fused Metal kernels, written and tuned by our in-house kernel agents for the model's dimensions, read weights packed for them and mapped zero-copy from disk. They ship precompiled: no Xcode, no compiler toolchain, nothing tuned on your machine.
• A memory plan computed for this machine. Context, KV capacity, and batch limits are worked out at startup from the memory Metal recommends, less the weights, the draft, and each request's state.

The launch post covers the design in depth.

More

• DEVELOPMENT.md: building from source, tests, model packages, and release packaging.
• Apache-2.0, see LICENSE. Model weights keep their own licenses.