Turboquant

https://en.wikipedia.org › wiki › TurboQuant
TurboQuant TurboQuant is an online vector quantization algorithm for compressing high dimensional Euclidean vectors while preserving their geometric structure It was proposed in 2025 by Amir

https://turbo-quant.com › how-to-use-turboquant
Step by step guide to getting started with TurboQuant KV cache compression Learn how to install set up locally and test with Llama and other LLMs

https://github.com › turboquant
TurboQuant compresses the cache to 4 bits from 16 using Google s TurboQuant algorithm ICLR 2026 No training data no calibration works with any model The result your GPU

https://vllm.ai › blog
TurboQuant a method for KV cache quantization recently gained significant traction in the community due to the large advertised savings in GPU memory from very low bit width

https://huggingface.co › ... › llama-cpp-turboquant-guide
This guide lets you run a local LLM server that can handle up to 100 000 tokens of context on a typical desktop GPU By building the provided Docker image supplying a HuggingFace access

https://ai2.work › blog › turboquant-how-google...
Google s TurboQuant compresses LLM KV caches to 3 bits with no accuracy loss cutting memory 6x and speeding H100 attention up to 8x Here s why it matters

https://turboquant.net
Original explainers benchmark interpretation and implementation notes covering TurboQuant KV cache compression and long context inference

https://towardsdatascience.com › qdrant-turboquant...
In early May of 2026 Qdrant released TurboQuant a new quantization method And they claimed that TurboQuant can reduce memory use without making retrieval quality too unstable TurboQuant

https://pypi.org › project › turboquant
TurboQuant compresses this cache to 4 bits per element from 16 cutting memory by 4x It does this using a clever trick from Google s paper rotate the vectors randomly then quantize