Big win for local LLM inference! This new inference engine runs LLMs 4x faster than llama.cpp: I ran Qwen3.5 9B on a base M5 MacBook Pro with three engines: - llama.cpp: 22.0 tokens/s - MLX: 25.1 tokens/s - uzu: 92.1 tokens/s The video below shows all three runs side by side. The first two numbers don't mean llama.cpp or MLX are badly built. But both are sitting right under a hard limit of local decoding. When an LLM generates text, every new token needs one full pass through the model, and that pass reads all of the model's weights from memory. A base M5 reads memory at about 153 GB/s. A 4-bit 9B model weighs about 5.2 GB. So at one token per pass, the ceiling is roughly 153 ÷ 5.2 ≈ 29 tokens/s, however fast the GPU computes. uzu gets past that ceiling by producing several tokens per pass instead of one: ↳ A small drafter (DFlash) proposes candidates for the next 16 positions at once. ↳ A tiny adapter (Weaver, 56.7M params) picks among them left to right, so the guesses form coherent text, and keeps a few alternative branches as a tree. ↳ The 9B model checks the whole tree in a single pass and keeps the longest path it agrees with. The main model still decides every token that ends up in the output. GitHub repo: github.com/trymirai/uzu (don't forget to star it 🌟) In my run, each pass of the 9B model produced 7.5 tokens on average. Ordinary decoding produces exactly 1. The weights and kernels are designed around this too. uzu ships its own 4-bit checkpoints, and on M5 chips it runs the verification step on the GPU's int8 matrix path. uzu is an open-source inference engine by Mirai Labs, written in Rust with Metal kernels. It has Swift, Python, TypeScript and Rust bindings, so the same engine runs inside a Mac or iOS app. I shared the GitHub repo above. If you want to dive deeper, my co-founder wrote a detailed breakdown of why local inference is memory-bound and how uzu's speculative decoding gets past it. Read it below.