Google's TurboQuant algorithm compresses LLM key-value caches to 3 bits with no accuracy loss. Memory stocks fell within ...
Google Research recently revealed TurboQuant, a compression algorithm that reduces the memory footprint of large language ...
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 ...
Compression algorithms for speech, audio, still images, and video are quite complicated and, more importantly, nearly always lossy. Thus, samples often change dramatically once they’re decompressed.
Google thinks it's found the answer, and it doesn't require more or better hardware. Originally detailed in an April 2025 ...
Compression reduces bandwidth and storage requirements by removing redundancy and irrelevancy. Redundancy occurs when data is sent when it’s not needed. Irrelevancy frequently occurs in audio and ...