Google researchers have published a new quantization technique called TurboQuant that compresses the key-value (KV) cache in large language models to 3.5 bits per channel, cutting memory consumption ...

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 ...

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 ...

The compression algorithm works by shrinking the data stored by large language models, with Google’s research finding that it can reduce memory usage by at least six times “with zero accuracy loss.” [ ...

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

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.

Researchers have developed a holographic data storage approach that stores and retrieves information in three dimensions by ...

Every second, scientific experiments produce a flood of data—so much that transmitting and analyzing it can slow down even the most advanced research. To help scientists better manage this data deluge ...

Lucas Downey is the co-founder of MoneyFlows, and an Investopedia Academy instructor. Somer G. Anderson is CPA, doctor of accounting, and an accounting and finance professor who has been working in ...

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