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

Large language models (LLMs) aren’t actually giant computer brains. Instead, they are effectively massive vector spaces in which the probabilities of tokens occurring in a specific order is ...

TurboQuant vector quantization targets KV cache bloat, aiming to cut LLM memory use by 6x while preserving benchmark accuracy ...

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.

SAN FRANCISCO--(BUSINESS WIRE)--Elastic (NYSE: ESTC), the Search AI Company, announced new performance and cost-efficiency breakthroughs with two significant enhancements to its vector search. Users ...

Dnotitia Inc. (Dnotitia), a leading AI and semiconductor company, today announced the release of an open-source platform for evaluating AI quantization techniques. Jointly developed through an ...

Google’s TurboQuant has the internet joking about Pied Piper from HBO's "Silicon Valley." The compression algorithm promises ...

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, ...

The algorithm achieves up to an eight-times performance boost over unquantized keys on Nvidia H100 GPUs.