Large language models (LLMs) aren’t actually giant computer brains. Instead, they are effectively massive vector spaces in which the probabilities of tokens occurring in a specific order is ...

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

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

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 ...

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 ...

A new compression technique from Google Research threatens to shrink the memory footprint of large AI models so dramatically ...