LatticeQuant is a research framework for KV cache compression in large language models, combining lattice quantization theory, directional distortion analysis, and attention-aware bit allocation.

Random rotation: Multiply the input vector by a fixed random orthogonal matrix. This makes each coordinate follow a known Beta(d/2, d/2) distribution. Lloyd-Max scalar quantization: Quantize each ...

The big picture: Google has developed three AI compression algorithms – TurboQuant, PolarQuant, and Quantized Johnson-Lindenstrauss – designed to significantly reduce the memory footprint of large ...

Even if you don’t know much about the inner workings of generative AI models, you probably know they need a lot of memory. Hence, it is currently almost impossible to buy a measly stick of RAM without ...

John Steinbach was shocked to receive a $281 electricity bill in January 2026—a huge spike from the roughly $100 he’d paid the previous month. “It’s just so far beyond any bill that I’ve ever had,” he ...

Abstract: Historically, the Vector Quantization (VQ) image compression algorithm was designed for single-core processors. Despite its simplicity, impressive bit rates, and good reconstructed image ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Birgitta Böckeler, Distinguished Engineer at ...

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 ...