AI hit the memory wall — now it needs a new context tier

Presented by SolidigmAs inference workloads evolve from discrete question-and-answer exchanges into persistent, multi-step agentic systems, GPU availability is no longer the most critical AI bottleneck. Instead, the bottleneck has migrated from compute to context, says Jeff Harthorn, AI applied research lead at Solidigm."Why context management has become a primary bottleneck, more than GPU availability or compute efficiency, is the question of 2026," says Harthorn. "GPUs have gotten dramatically cheaper per FLOP. Model architectures and inference serving engines have all gotten much more efficient. But the thing that's grown faster than both of those is context. The persistent state that has to live between sessions has grown even faster than context itself."It's happening as context windo…