The next decade of AI memory.

Why memory is the defining constraint of the AI decade.

Modern AI workloads are no longer bounded by raw compute. Bandwidth, capacity, and the cost of moving data now define what’s feasible at every scale — from frontier training clusters to on-device inference.

This paper lays out the architectural decisions shaping the next generation of AI memory systems, the trade-offs between stacked, low-power, and processing-in-memory designs, and what workload-aligned memory looks like in production.

What’s inside

• The memory wall: why compute growth has outpaced bandwidth and capacity
• Comparing HBM, LPDDR6, and processing-in-memory across training and inference
• Total-cost-of-ownership levers driven by memory choice
• The role of standards-based design in real-world deployment
• What “workload-aligned memory” looks like from datacenter to edge