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.

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

Google thinks it's found the answer, and it doesn't require more or better hardware. Originally detailed in an April 2025 paper, TurboQuant is an advanced compression algorithm that’s going viral over ...

SIEVE is a new approach to web caching that's simpler and more effective than today's state-of-the-art algorithms, its creators claim — and big tech companies are taking notice. When you purchase ...

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 ...

Part 2 looks at the tradeoffs between program and data cache optimizations, and shows how to choose the best compromise. As we saw in the first two parts of this series, cache optimization is often ...

Google's new TurboQuant algorithm drastically cuts AI model memory needs, impacting memory chip stocks like SK Hynix and Kioxia. This innovation targets the AI's 'memory' cache, compressing it ...