The real bottleneck in AI inference is shifting from pure compute to memory hierarchy architecture.

As LLM context windows expand and agentic workflows become more complex, KV cache is no longer just a small GPU buffer. It is evolving into a multi-tier system spanning HBM, DDR, pooled memory, SSDs, and networked data lakes.

The future of AI infrastructure is not only about GPU scaling — it is about memory orchestration. Whoever controls HBM bandwidth, memory pooling, context storage, and high-speed SSD architecture will have a stronger position in next-generation AI inference platforms.