What will Google's TurboQuant actually change for our local setups, and specifically mobile inference?

Hi everyone, I've been reading up on Google's recent TurboQuant announcement from a few days ago (compressing the KV cache down to 3-4 bits with supposedly zero accuracy loss), and I'm trying to wrap my head around the practical implications for our daily setups. We already have great weight quantization formats like GGUF. but since TurboQuant specifically targets the KV cache rather than the model weights, I have a few questions for those who have dug into the paper or tried the early mlx / llama.cpp forks: General Local Processing Throughput vs.