RotorQuant Claims 10-19x Faster KV-Cache Compression Than TurboQuant Using Clifford Algebra

r/LocalLLaMA / GitHub·medium signalXBlueskyLinkedInCopy link

A community developer released RotorQuant, reimplementing TurboQuant's KV-cache compression approach using Clifford Algebra Vector Quantization with CUDA and Metal shader implementations. The project claims 10-19x faster compression than Google's TurboQuant (ICLR 2026) with 44x fewer parameters needed. Code is available on GitHub (tonbistudio/turboquant-pytorch). At 116 upvotes and 25 comments on r/LocalLLaMA, this is an early-stage community project — claims need independent verification, but the approach of applying geometric algebra to quantization is novel.