Run 500B+ Parameter LLMs Locally on a Mac Mini
Hi HN, I built OpenGraviton, an open-source AI inference engine that pushes the limits of running extremely large LLMs on consumer hardware. By combining 1.58-bit ternary quantization, dynamic sparsity with Top-K pruning and MoE routing, and mmap-based layer streaming, OpenGraviton can run models far larger than your system RAM—even on a Mac Mini. Early benchmarks: TinyLlama-1.1B drops from ~2GB (FP16) to ~0.24GB with ternary quantization. At 140B scale, models that normally require ~280GB fit within ~35GB packed. Optimized for Apple Silicon with Metal + C++ tensor unpacking, plus…
In plain words
OpenGraviton is an open-source AI inference engine that enables running very large language models on consumer hardware like Mac Mini. It uses ternary quantization, dynamic sparsity pruning, and layer streaming to compress massive models—reducing a 140-billion-parameter model from 280GB to roughly 35GB. The tool is optimized for Apple Silicon and includes speculative decoding to speed up text generation. It's for developers and researchers who want to run state-of-the-art LLMs locally without expensive infrastructure.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN, I built OpenGraviton, an open-source AI inference engine that pushes the limits of running extremely large LLMs on consumer hardware. By combining 1.58-bit ternary quantization, dynamic sparsity with Top-K pruning and MoE routing, and mmap-based layer streaming, OpenGraviton can run models far larger than your system RAM—even on a Mac Mini. Early benchmarks: TinyLlama-1.1B drops from ~2GB (FP16) to ~0.24GB with ternary quantization. At 140B scale, models that normally require ~280GB fit within ~35GB packed. Optimized for Apple Silicon with Metal + C++ tensor unpacking, plus speculative decoding for faster generation. Check benchmarks, architecture, and details here: https://opengraviton.github.io GitHub: https://github.com/opengraviton This project isn’t just about squeezing massive models onto tiny hardware—it’s about democratizing access to giant LLMs without cloud costs. Feedback, forks, and ideas are very welcome!
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