Apple Silicon Inference Engine: Kapitsa Kapitsa, an inference engine for Apple silicon from developer nace-ai, reports the fastest decode in 32 of 36 model-and-Mac pairs against nine competing engines, reaching up to 2.9× MLX, according to the project's published benchmarks. The engine runs Qwen, Nemotron and Recursive's Flint models locally and launches coding agents including opencode, Claude Code, Codex and pi on a local model while serving OpenAI and Anthropic APIs; on a 16 GB M1 Pro it decodes Flint at 61 tok/s versus llama.cpp's 43 on the same 2-bit GGUF. Kapitsa's KFlash tree speculative decoding reaches up to 3.5× llama.cpp and 2.2× oMLX, and its SSD prefix cache starts a 16K-token follow-up on the 35B model in 0.16 s instead of 17 s. Kapitsa is an inference engine for Apple silicon that runs Qwen, Nemotron and Recursive's Flint models locally, built for coding agents. It starts opencode, Claude Code, Codex or pi on a local model, and serves OpenAI and Anthropic APIs. Quickstart quickstart · CLI cli · Benchmarks benchmarks · Models models · How KFlash works https://github.com/nace-ai/kapitsa/blob/main/docs/kflash.md · Docs https://github.com/nace-ai/kapitsa/blob/main/docs/usage.md - 2026/10 🪶 Flint on a 16 GB Mac: decodes at 61 tok/s vs llama.cpp's 43 on the same 2-bit GGUF. --model flint takes unsloth's 2-bit GGUF on 16-24 GB Macs: its routed experts run as stored, the rest requantized to 3-6 bits. 61 tok/s decode on an M1 Pro, in 11.6 GB numbers https://github.com/nace-ai/kapitsa/blob/main/docs/benchmarks.md recursive-flint-40b-a25b-with-2-bit-experts-m1-pro-16-gb-2026-10-22 . - 2026/10 🪨 Recursive's Flint 40B-A2.5B: Kapitsa reads prompts faster than Recursive's own engine and decodes faster than llama.cpp. On the M4 Pro every Kapitsa run read 4K and 16K prompts faster than every run of rec 0.1.0. On the M5 Pro, since Flint's attention moved to the tensor units 345 , both runs read 4K and 16K prompts faster than rec's and llama.cpp's; earlier builds read slower, and both runs carry harness flags. On both Macs every Kapitsa run decodes faster than llama.cpp M4 Pro https://github.com/nace-ai/kapitsa/blob/main/docs/benchmarks.md recursive-flint-40b-a25b-m4-pro-2026-10-22 , M5 Pro https://github.com/nace-ai/kapitsa/blob/main/docs/benchmarks.md recursive-flint-40b-a25b-m5-pro-2026-10-22 . - 2026/10 🚀 KFlash: tree speculative decoding, up to 3.5× llama.cpp and 2.2× oMLX. One verify pass checks a whole tree of the drafter's guesses how it works https://github.com/nace-ai/kapitsa/blob/main/docs/kflash.md . - 2026/10 🏆 Kapitsa: the fastest decode in 32 of 36 model and Mac pairs against nine engines, up to 2.9× MLX poster benchmarks . - 2026/10 🤝 KFlash budget: subagents decode together. Three requests at once share one verify pass, up to +23% total throughput on the 27B. - 2026/10 ⚡ QMT prefill: read the prompt up to 1.5× faster on M5. Metal 4 matrix units for the prompt 4B, M5 Pro . - 2026/10 💾 SSD prefix cache: restart the server, keep the conversation. A 16K-token follow-up on the 35B starts in 0.16 s instead of 17 s , about 100× --disk-cache . - 2026/10 🧠 Prefix reuse: subagents start warm. A new question over the shared system prompt answers in 0.06 s instead of 1.9 s 4K prompt, 4B, M5 Pro . - 2026/10 🧪 Output check: speed has to prove it kept the answers. A default changes only after its logits stay within twice the model's noise floor how https://github.com/nace-ai/kapitsa/blob/main/CONTRIBUTING.md output-check . - 2026/10 📊 kapitsa bench compare : see it on your own Mac. The same test against every engine installed here, in one command. Requirements: a Mac with Apple silicon M1 or newer and macOS 14 Sonoma or later. brew install nace-ai/tap/kapitsa or: curl -fsSL https://raw.githubusercontent.com/nace-ai/kapitsa/main/install.sh | bash kapitsa opencode --model 35b also: claude, codex, pi