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

Vector quantisation and its associated learning algorithms form an essential framework within modern machine learning, providing interpretable and computationally efficient methods for data ...

A paper from Google could make local LLMs even easier to run.

What is Google TurboQuant, how does it work, what results has it delivered, and why does it matter? A deep look at TurboQuant, PolarQuant, QJL, KV cache compression, and AI performance.

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

Learn why Google’s TurboQuant may mark a major shift in search, from indexing speed to AI-driven relevance and content discovery.

Google's TurboQuant reduces the KV cache of large language models to 3 bits. Accuracy is said to remain, speed to multiply.

New capabilities deliver up to 5X faster filtered vector search, improved ranking quality, and lower infrastructure costs to unlock scalable, cost-efficient AI applications SAN FRANCISCO, July 30, ...

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