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

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

A small error-correction signal keeps compressed vectors accurate, enabling broader, more precise AI retrieval.

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

With TurboQuant, Google promises 'massive compression for large language models.' ...

Google's TurboQuant combines PolarQuant with Quantized Johnson-Lindenstrauss correction to shrink memory use, raising ...

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

Memory prices are plunging and stocks in memory companies are collapsing following news from Google Research of a ...

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 ...

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