TurboQuant: Multiplied Efficiency for Language Models

Google has announced TurboQuant, a new compression technique designed to drastically reduce the memory requirements of Key/Value (KV) caches used by large language models (LLMs). A key feature of TurboQuant is its ability to compress these caches down to just 3 bits, without compromising model accuracy.

Improved Performance on Nvidia H100

Tests conducted by Google indicate a performance increase of up to 8x on Nvidia H100 GPUs. This improvement is significant, especially in scenarios where memory capacity represents a bottleneck. The technology promises to reduce memory capacity requirements by at least six times.

Implications for Deployment

The reduction in memory requirements and the increase in inference speed thanks to TurboQuant could have a significant impact on the deployment decisions of LLM models.