Google Introduces TurboQuant: A New Compression Algorithm that Reduces LLM Key-Worth Cache Reminiscence by 6x and Delivers As much as 8x Speedup, All with Zero Accuracy Loss
The scaling of Giant Language Fashions (LLMs) is more and more constrained by reminiscence communication overhead between Excessive-Bandwidth Reminiscence (HBM) and SRAM. Particularly, the Key-Worth (KV) cache dimension scales with each mannequin dimensions and context size, creating a major bottleneck for long-context inference. Google analysis staff has proposed TurboQuant, a data-oblivious quantization framework designed to…
