Large-scale applications, such as generative AI, recommendation systems, big data, and HPC systems, require large-capacity ...

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

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

Google has unveiled a new technique that could dramatically reduce the amount of memory required to run artificial intelligence (AI) models. The breakthrough, called TurboQuant, was announced by ...

TL;DR: Google developed three AI compression algorithms-TurboQuant, PolarQuant, and Quantized Johnson-Lindenstrauss-that reduce large language models' KV cache memory by at least six times without ...

Google's (GOOG)(GOOGL) 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 ...

SysMain' was draining my computer's background memory. Here's how to find the biggest culprits behind your sluggish PC.

The Ryzen 9 9950X3D2 doubles down on AMD's V-Cache formula by equipping each of its two CCDs with stacked cache memory. The design results in a ...

Following several leaks, AMD has announced that its Ryzen 9 9950X3D2 desktop processor packs even more 3D V–Cache, letting the CPU harness a larger pool of SRAM for gaming and other workloads. The ...

Researchers at North Carolina State University have developed a new AI-assisted tool that helps computer architects boost ...

An AI tool improves processor speed by studying cache use and helping make memory decisions without repeated testing and ...

Large language models (LLMs) aren’t actually giant computer brains. Instead, they are massive vector spaces in which the ...