A paper from Google could make local LLMs even easier to run.

Google researchers have published a new quantization technique called TurboQuant that compresses the key-value (KV) cache in ...

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

The compression algorithm works by shrinking the data stored by large language models, with Google’s research finding that it can reduce memory usage by at least six times “with zero accuracy loss.” [ ...

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

That much was clear in 2025, when we first saw China's DeepSeek — a slimmer, lighter LLM that required way less data center ...

Traditional encryption methods have long been vulnerable to quantum computers, but two new analyses suggest a capable enough ...

Researchers have developed a holographic data storage approach that stores and retrieves information in three dimensions by ...