For most investors, how much a stock's price changes over time is important. Not only can it impact your investment portfolio, but it can also help you compare investment results across sectors and ...

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

Google unveils TurboQuant, PolarQuant and more to cut LLM/vector search memory use, pressuring MU, WDC, STX & SNDK.

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

When standard RAG pipelines retrieve redundant conversational data, long-term AI agents lose coherence and burn tokens.

Within 24 hours of the release, community members began porting the algorithm to popular local AI libraries like MLX for Apple Silicon and llama.cpp.

Google’s TurboQuant has the internet joking about Pied Piper from HBO's "Silicon Valley." The compression algorithm promises to shrink AI’s “working memory” by up to 6x, but it’s still just a lab ...

Legacy historians were designed for a slower era. Today's high-frequency sensor data demands millisecond response times.

Ashley is a lead editor of mortgages and loans at Forbes Advisor. She graduated from Utah Tech University with a bachelor’s in English with an emphasis in creative writing. She began her career ...

In a new co-authored book, Professor and Chair of Psychology and Neuroscience Elizabeth A. Kensinger points out some surprising facts about how memories work Explaining the science behind memory and ...

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