Surging KV Cache demand, driven by larger context windows and batch sizes, has become the primary memory bottleneck for AI inference in the first half of 2026. This constraint is leading to the development of new memory hierarchy tiers through CXL technology and KV Cache offloading, alongside footprint compression techniques like quantization.
KV Cache demand now dictates AI inference memory requirements, shifting the binding constraint from compute to memory and driving new CXL-based solutions.