Google's #TurboQuant is a compression mechanism for Key-Value Cache — the 'memory' of an #LLM.

It is derived from #PolarQuant which converts KV vectors from Cartesian to polar co-ordinates.

Early tests (admittedly by Google) show 6x reduction in memory usage.

This seems quite important for #AI.

Google's TurboQuant Algorithm Slashes LLM Memory Use by 6x Google has published TurboQuant, a KV cache compression algorithm that cuts LLM memory usage by 6x with zero accuracy loss, spurring rapid community adoption.

winbuzzer.com/2026/03/26/g...

Google's TurboQuant Slashes LLM Memory Use by 6x

#AI #Google #Turboquant #Polarquant #LLMs #AIResearch #AIInference #GoogleAI #MachineLearning #DeepLearning #BigTech #DataCenters #CloudComputing #GoogleDeepMind

TurboQuant claims 3-bit quantization with "zero accuracy loss" for LLMs. Is this the holy grail for local AI, or is an engineer's skepticism about this "magic" solution warranted?

thepixelspulse.com/posts/turboquant-3-bit-q...

#turboquant #polarquant #qjl