That engineering reality is unfortunately not matched by the rising prices of memory. TrendForce forecasts steep contract price increases for conventional DRAM and server DRAM in Q1 2026, citing a widening supply-demand gap and rising demand tied to cloud service providers and AI infrastructure. Whether your organization feels that as pressure on pricing, allocation, or both, the implication is the same: Memory is becoming a primary infrastructure constraint.
This is why standards like Compute Express Link (CXL) are becoming more architecturally relevant. CXL is a cache-coherent interconnect designed to attach memory and other devices, allowing systems to expand memory capacity while paving the way for flexible pooling and composability over time. In practical terms, it gives platform teams greater control over memory configuration and sharing, helping keep expensive accelerators productive as workloads outgrow local HBM capacity and DRAM availability becomes more constrained.
The Hidden Cost of AI Scale: Memory Dictates GPU Efficiency
Most organizations have become fluent in GPU math: tokens per second, batch size, and utilization. In production, a less visible number often dominates unit economics: how much time GPUs spend waiting.
