WARP

WARP (formerly WASTE) runs AI models far bigger than your computer's memory. It keeps the always-used core of a mixture-of-experts model in RAM and streams the rest off your SSD per token. The headline: the full 2.78-trillion-parameter Kimi K3 on a 64 GB MacBook Pro. It's a dependency-free C engine from the SQLite Cloud team, free under Apache 2.0.

Kimi K3 is the stunt. The useful part is the mid-size models: GLM-5.3-Flash runs at about 3.9 tokens per second on that same MacBook and still works on a 16 GB machine, and DeepSeek-V4.1-Flash manages about 3.8. You get a CLI, an embeddable C library and an optional OpenAI-compatible server. Setup is real work: you download and convert the weights yourself.

No paid tier. Solo tinkerers: free, start with the small Kimi-Linear. Small teams: free for offline experiments. Larger teams: this is not a serving stack, use vLLM or llama.cpp on GPUs.

The catch is speed and disk. K3 runs under one token per second and needs about 1 TB of NVMe, and prefill is as slow as decode, so long prompts take minutes. The format and API aren't frozen, and the README says LLMs write the code while humans steer.