Nvidia reportedly tightens grip on AI inference market despite growing competition

NVIDIA’s expansion from training to inference isn’t just market capture—it’s ecosystem entrenchment via CUDA lock-in. This forces cloud providers and AI startups into a brutal trade-off: abandon NVIDIA and rewrite entire software stacks, or accept eroding margins. While Taiwan, China and South Korean foundries benefit from surging H20/B200 orders, U.S. export controls—now extending to inference chips—are inflating compliance costs. Competitors are reacting decisively: AMD is fast-tracking MI300X software compatibility, while Google and Amazon double down on TPUs and Trainium to close the loop. Over the next 18 months, the inference landscape will bifurcate: data centers remain NVIDIA-dominated, but edge devices will see rapid adoption of RISC-V paired with domain-specific NPUs due to power efficiency and localization demands. This is no longer a chip race—it’s a battle for stack sovereignty.