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

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 ...

Google’s TurboQuant has the internet joking about Pied Piper from HBO's "Silicon Valley." The compression algorithm promises ...

Google Research recently revealed TurboQuant, a compression algorithm that reduces the memory footprint of large language ...

Google's TurboQuant algorithm compresses LLM key-value caches to 3 bits with no accuracy loss. Memory stocks fell within ...

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 ...

The duel is age-old: on one side, cold, methodical and exhaustive ‘calculation’; on the other, the decision-maker’s dazzling intuition, that often indefinable ‘flair’. Since the advent of Big Data ...

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

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

Every second, scientific experiments produce a flood of data—so much that transmitting and analyzing it can slow down even ...