Source: SiliconANGLE
Solidigm's pivot toward inference-optimized storage reflects a market shift: as AI moves from training to deployment, the bottleneck shifts from compute to data movement. Inference engines at scale need fast feeds. That matters because inference is where AI generates revenue—chatbots, recommendations, autonomous systems. Companies that control the storage layer in that pipeline gain leverage over the AI stack, similar to how Nvidia dominates training.