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

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, ...

This is really where TurboQuant's innovations lie. Google claims that it can achieve quality similar to BF16 using just 3.5 ...

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

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.

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

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

Google researchers have published a new quantization technique called TurboQuant that compresses the key-value (KV) cache in ...