Long-context generation makes the KV cache hard to ignore. Every generated token reuses keys and values from previous tokens. As the context grows, those cached tensors grow with it. So the natural first idea is simple: Compress the KV cache, store fewer bytes, and get faster generation. We tested that idea while exploring TurboQuant-style cache compression in a Hugging Face transformers fork. Important scope note: This is not a claim that the official TurboQuant research idea "does not work." The external context is: Google Research introduced TurboQuant as a compression method for extreme KV-cache and vector compression: https://research.google/blog/turboquant-redefining-ai-efficiency-with-extreme-compression/ The TurboQuant paper describes an online vector quantization approach with residual correction for inner-product preservation: https://arxiv.org/abs/2504.19874 Hugging Face transformers exposes several cache strategies, including dynamic and quantized caches:…