Prerequisites

• I am running the latest code. Mention the version if possible as well.
• I carefully followed the README.md.
• I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed).
• I reviewed the Discussions, and have a new and useful enhancement to share.

Feature Description

Google recently announced TurboQuant - a new quantization method which compresses KV cache using polar coordinates, shrinking memory requirements.

https://research.google/blog/turboquant-redefining-ai-efficiency-with-extreme-compression/

Results with MLX seems to be promising as well https://x.com/i/status/2036611007523512397

Motivation

This would allow to run bigger models on smaller hardware

Possible Implementation

I'm not submitting a PR because I'm literally playing with it with claude now, but if it can help I'm experimenting at https://github.com/mudler/llama.cpp/tree/feat/turbo-quant and currently builds/starts correctly. Still evaluating it.