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

Google developed a new compression algorithm that will reduce the memory needed for AI models. If this breakthrough performs ...

Memory prices are plunging and stocks in memory companies are collapsing following news from Google Research of a ...

Google's TurboQuant combines PolarQuant with Quantized Johnson-Lindenstrauss correction to shrink memory use, raising ...

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

Memory stocks continued to struggle in early trading Tuesday amid fears over Google's AI compression algorithm.

With TurboQuant, Google promises 'massive compression for large language models.' ...

Google thinks it's found the answer, and it doesn't require more or better hardware. Originally detailed in an April 2025 paper, TurboQuant is an advanced compression algorithm that’s going viral over ...

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.

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