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 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 ...

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 continued to be pressured on Friday after a sell-off in social media stocks and chip stocks sent the tech-heavy ...

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

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 ...