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

The technique reduces the memory required to run large language models as context windows grow, a key constraint on AI ...

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

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

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

The algorithm achieves up to an eight-times performance boost over unquantized keys on Nvidia H100 GPUs.

Abstract: Data Structures and Algorithms (DSA) is fundamental to computer science education, yet novice learners face significant challenges in grasping abstract concepts and their system-level ...

This year, there won't be enough memory to meet worldwide demand because powerful AI chips made by the likes of Nvidia, AMD and Google need so much of it. Prices for computer memory, or RAM, are ...

AI hardware needs to become more brain-like to meet the growing energy demands of real-world applications, according to researchers. In a study published in Frontiers in Science, scientists from ...