TurboQuant: Google's 6x KV Cache Compression and the Quiet Economics of Long Context AI
Google Research's TurboQuant compresses the LLM key value cache to roughly three bits per coordinate with near zero accuracy loss, delivering at least six times less memory and up to eight times faster attention on NVIDIA H100 GPUs. We unpack how its two stage design pairs a training free random rotation with a one bit correction step, why a 70B model's 128K context cache shrinks from about 40GB to under 7GB, and what that means for the cost of long context AI everywhere.
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