Source: SiliconANGLE
Inference workloads now dwarf training in total compute spend, creating pressure to optimize not just raw speed but cost per query. Storage architecture has become the primary control surface. Companies like Anthropic and inference-specialist startups layer fast cache (SRAM/HBM), warm storage (NVMe), and cold storage (HDDs) to reduce the per-token cost of serving large models. The competition is shifting from model capability to operational margins. This favors infrastructure vendors and chip companies selling tiered solutions, while pushing large model providers toward capital-efficient serving rather than bigger parameter counts.