Google's TurboQuant algorithm compresses LLM key-value caches to 3 bits with no accuracy loss. Memory stocks fell within ...

Google has published TurboQuant, a KV cache compression algorithm that cuts LLM memory usage by 6x with zero accuracy loss, ...

A small error-correction signal keeps compressed vectors accurate, enabling broader, more precise AI retrieval.

Google Research recently revealed TurboQuant, a compression algorithm that reduces the memory footprint of large language ...

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 ...

Google thinks it's found the answer, and it doesn't require more or better hardware. Originally detailed in an April 2025 ...

DDR5 RAM prices are finally dropping after months of inflation, according to Wccftech. Consumers and hardware manufacturers ...

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

The Google Research team developed TurboQuant to tackle bottlenecks in AI systems by using "extreme compression".

Google's new TurboQuant algorithm drastically cuts AI model memory needs, impacting memory chip stocks like SK Hynix and ...

Memory stocks fell Wednesday despite broader technology sector strength, with shares dropping after Google unveiled ...

The biggest memory burden for LLMs is the key-value cache, which stores conversational context as users interact with AI ...