Gemma 3 inference in pure C++ with Metal acceleration
Pure C++23 LLM inference for Apple Silicon chips. Contribute to ybubnov/metalchat development by creating an account on GitHub.
Gemma 3 inference in pure C++ with Metal acceleration launched on July 4, 2026 with 6 votes, #3,356 of 24,623 launches that month and more than 75% of that year's launches.
What it does
MetalChat is a Metal -accelerated C++ framework and command line interpreter for inference of Meta Llama and Google Gemma models. The library API and CLI are under active development, therefore they may change without any deprecation notice. See issues tab for the list of known issues or missing features. See the getting started guide for using MetalChat as a library and command line guide for using MetalChat to interact with LLM model from the command line. brew tap ybubnov/metalchat https://github.com/ybubnov/metalchat brew install metalchat Alternatively you could build a Conan package locally using dependencies downloaded from the Conan registry. After that, you could use the MetalChat…from github.com
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We made our own inference engine for Apple Silicon2025 · github.com · ▲186We wrote our inference engine on Rust, it is faster than llama cpp in all of the use cases. Your feedback is very welcomed. Written from scratch with idea that you can add support of any kernel and platform.
Tiny-vLLM – high performance LLM inference engine in C++ and CUDAMay 2026 · github.com · ▲205Build your own high performance LLM inference engine in C++ and CUDA - a smaller version of vLLM - jmaczan/tiny-vllm
Speeding up LLM inference 2x times (possibly)2024 · asciinema.org · ▲419Here's a project I've been working on for the last few months. It's a new (I think) algorithm, that allows to adjust smoothly - and in real time - how many calculations you'd like to do during inference of an LLM model. It seems that it's possible to do just 20-25% of weight multiplications instead of all of them, and still get good inference results. I implemented it to run on M1/M2/M3 GPU. The mmul approximation itself can be pushed to run 2x fast before the quality of output collapses. The inference speed is just a bit faster than Llama.cpp's, because the rest of implementation…
Open-source engine running Gemma 4 26B in 2 GB RAM on any M-series MacJul 2026 · github.com · ▲919Hi HN, I built a specialized inference engine for running 4-bit Gemma 4 26B-A4B-IT on any M-series Mac using about 2 GB of RAM. It is called TurboFieldfare and is written in Swift and Metal. I have always adored on-device AI. It feels like magic that you can run a powerful NN on your Mac or iPhone. So I wanted to push the limits a bit and run a model whose weights don’t fit in memory. The model’s 4-bit quantized weights occupy roughly 14 GB, which makes running it with conventional inference tools almost impossible on an 8 GB or even 16 GB Mac once the OS, applications, and KV cache are…
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Port of OpenAI's Whisper model in C/C++2022 · github.com · ▲399Hi HN, OpenAI recently released a model for automatic speech recognition called Whisper [0]. I decided to reimplement the inference of the model from scratch using C/C++. To achieve this I implemented a minimalistic tensor library in C and ported the high-level architecture of the model in C++. The entire code is less than 8000 lines of code and is contained in just 2 source files without any third-party dependencies. The Github project is here: https://github.com/ggerganov/whisper.cpp With this implementation I can very easily build and run the model - “make…
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