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

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

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

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

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

Tech stocks sank on Thursday amid uncertainty over US-Iran talks and as a landmark trial verdict opened social media ...

Tech stocks remained under pressure on Monday after a brutal sell-off last week that sent the tech-heavy Nasdaq Composite ...

Google has published TurboQuant, a KV cache compression algorithm that cuts LLM memory usage by 6x with zero accuracy loss, ...

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

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

Google researchers have proposed TurboQuant, a method for compressing the key-value caches that large language models rely on ...