Draft

Thats freaking awesome: Google Research has introduced TurboQuant, a compression algorithm (presenting at ICLR 2026) that shrinks the memory footprint of large language models by at least 6x, without any retraining or drop in accuracy. It works by converting data into a polar coordinate system that eliminates storage overhead, then applying a 1-bit error-correction step to clean up remaining distortion. In tests on Gemma and Mistral models, its 4-bit version delivered up to 8x faster processing on H100 GPUs while matching full-precision quality across tasks like question answering and code generation. The technique also outperformed existing methods in vector search, the technology behind modern semantic search engines.