Large language models (LLMs) aren’t actually giant computer brains. Instead, they are massive vector spaces in which the ...

TurboQuant vector quantization targets KV cache bloat, aiming to cut LLM memory use by 6x while preserving benchmark accuracy ...

Google researchers have proposed TurboQuant, a two-stage quantization method that, according to a recent arXiv preprint, can ...

Google introduces TurboQuant, a compression method that reduces memory usage and increases speed ...

What Google's TurboQuant can and can't do for AI's spiraling cost ...

Within 24 hours of the release, community members began porting the algorithm to popular local AI libraries like MLX for Apple Silicon and llama.cpp.

Fine-tuning large language models (LLMs) might sound like a task reserved for tech wizards with endless resources, but the reality is far more approachable—and surprisingly exciting. If you’ve ever ...