The Blessing of Dimensionality: How TurboQuant Uses the JL Lemma to Compress KV Caches with Zero Bias
If you are running local LLMs, you already know the bottleneck isn’t compute; it’s memory. Specifically, the KV cache. As your context window grows, storing Keys and Values for every token eats your VRAM alive. On a standard 16GB consumer GPU, you are typically hard-capped around an 8K context length after loading the model weights.