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

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

Google has released a new compression algorithm this week that it says can shrink the memory an AI model needs during inference by at least six times—.

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

Memory stocks declined Wednesday as investors reacted to Google’s announcement of TurboQuant, a new compression algorithm ...

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

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

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

Sandisk stock fell ~7% after Google TurboQuant, but compression applies only to KV cache, not total storage demand. Learn why SNDK stock is upgraded to strong buy.

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

The technique reduces the memory required to run large language models as context windows grow, a key constraint on AI ...