Running large language models locally on personal hardware has always presented severe hardware bottlenecks for developers and enthusiasts. While standard weight quantization techniques like GGUF and EXL2 effectively shrink static model weights, extended context windows quickly exhaust system memory during active text generation. Now, groundbreaking research from Google DeepMind introduces TurboQuant, an innovative compression algorithm that directly targets the dynamic key-value memory space. By compressing runtime context data down to just 3 bits per value without degrading accuracy, this technology allows local hardware setups to handle ultra-long prompts effortlessly. The… Read More
Google Researchers Unveil “TurboQuant”: KV Cache Compression to Slash Local AI Memory Usage by 70%
