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

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

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 ...

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

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 ...

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

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.” [ ...

A more efficient method for using memory in AI systems could increase overall memory demand, especially in the long term.

At its core, the TurboQuant algorithm minimizes the space required to store memory while also preserving model accuracy. To ...

Google explains why it doesn't matter that websites are getting heavier and the reason has everything to do with SEO.