Why it matters: Cloudflare is attacking the memory-bandwidth bottleneck in LLM serving rather than only buying more GPUs. Its post reports 15-22% model-size reduction, about 3 GB VRAM saved on Llama 3.1 8B, and open-sourced GPU kernels.
A popular r/LocalLLaMA post revived attention around Google Research’s TurboQuant by tying it directly to local inference constraints. The method’s reported 3-bit KV cache compression and 6x memory reduction make it relevant well beyond research headlines, but its practical value will depend on whether it reaches real deployment stacks.