OpenGraviton – Run 500B+ parameter models on a consumer Mac Mini
Hi HN, I built OpenGraviton, an open-source AI inference engine designed to push the limits of running extremely large models on consumer hardware. The system combines several techniques to drastically reduce memory and compute requirements: • 1.58-bit ternary quantization ({-1, 0, +1}) for ~10x compression • dynamic sparsity with Top-K pruning and MoE routing • mmap-based layer streaming to load weights directly from NVMe SSDs • speculative decoding to improve generation throughput These allow models far larger than system RAM to run locally. In early benchmarks, OpenGraviton reduced…
In plain words
OpenGraviton is an open-source AI inference engine that enables running extremely large language models on consumer Mac hardware by combining ternary quantization, dynamic sparsity, layer streaming from NVMe storage, and speculative decoding. It reduces model memory requirements by up to 10 times, allowing 500 billion parameter models to run on machines with limited RAM. The system is optimized for Apple Silicon and targets developers and researchers who want to run large AI models locally without expensive server 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 designed to push the limits of running extremely large models on consumer hardware. The system combines several techniques to drastically reduce memory and compute requirements: • 1.58-bit ternary quantization ({-1, 0, +1}) for ~10x compression • dynamic sparsity with Top-K pruning and MoE routing • mmap-based layer streaming to load weights directly from NVMe SSDs • speculative decoding to improve generation throughput These allow models far larger than system RAM to run locally. In early benchmarks, OpenGraviton reduced TinyLlama-1.1B from ~2.05GB (FP16) to ~0.24GB using ternary quantization. Synthetic stress tests at the 140B scale show that models which would normally require ~280GB FP16 can fit within ~35GB when packed with the ternary format. The project is optimized for Apple Silicon and currently uses custom Metal + C++ tensor unpacking. Benchmarks, architecture, and details: https://opengraviton.github.io GitHub: https://github.com/opengraviton
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