TurboQuant, a compression algorithm that optimally addresses the challenge of memory overhead in vector quantization, will likely lead to the usage of more intensive AI applications rather than ...

Google (GOOG)(GOOGL) revealed a set of new algorithms today designed to reduce the amount of memory needed to run large language models and vector search engines. Shares of major memory and storage ...

Google thinks it's found the answer, and it doesn't require more or better hardware. Originally detailed in an April 2025 ...

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

Google has unveiled a new AI memory compression technology called TurboQuant, and the announcement has already had a ...

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

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

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, ...

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

Google has announced TurboQuant, a highly efficient AI memory compression algorithm, humorously dubbed 'Pied Piper' by the ...