The technique reduces the memory required to run large language models as context windows grow, a key constraint on AI ...
Google's TurboQuant algorithm compresses LLM key-value caches to 3 bits with no accuracy loss. Memory stocks fell within ...
Within 24 hours of the release, community members began porting the algorithm to popular local AI libraries like MLX for ...
Google has published TurboQuant, a KV cache compression algorithm that cuts LLM memory usage by 6x with zero accuracy loss, ...
The biggest memory burden for LLMs is the key-value cache, which stores conversational context as users interact with AI ...
The compression algorithm works by shrinking the data stored by large language models, with Google’s research finding that it can reduce memory usage by at least six times “with zero accuracy loss.” [ ...
The original version of this story appeared in Quanta Magazine. One July afternoon in 2024, Ryan Williams set out to prove himself wrong. Two months had passed since he’d hit upon a startling ...