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

Presented by Solidigm As 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.