TurboQuant KV cache compression sparks HBM panic

The market's "panic-sell" reaction to TurboQuant reflects a fundamental misunderstanding of AI memory bottlenecks and the difference between training and inference demand. TurboQuant targets the KV cache (inference memory), while the bulk of HBM demand is driven by training memory (activations, gradients, optimizer states), which remains untouched by this method. Commercial inference baselines already operate at 4-8 bits; the "6x" improvement is benchmarked against 16-bit precision, making the real-world gain much smaller than the headlines suggest. Furthermore, despite the paper circulating since early 2025, wide-scale deployment has been slow, suggesting integration hurdles or limited immediate necessity for existing architectures. This is the second instance of an efficiency paper causing an irrational memory stock sell-off, following a similar pattern observed after the DeepSeek release.