Nvidia controls an estimated 80% to 85% of AI GPU shipments by value. AMD's MI300X has captured meaningful incremental share in inference workloads specifically, where the memory bandwidth advantages of the HBM3 stack are most visible. The performance advantage in training workloads, where Nvidia's H100 and H200 dominate, is less clear.
The structural reason hyperscalers want a second GPU supplier is straightforward: single-vendor dependency on critical infrastructure is a supply chain risk they actively manage. Microsoft, Meta, and others have publicly committed to qualifying AMD silicon. Whether that commitment translates into sustained high-volume purchases at competitive margins is a different question.
The software ecosystem gap remains the most credible bear argument. ROCm, AMD's CUDA equivalent, has improved materially over the past two years, but CUDA's dominance is not merely a software product. It is a developer ecosystem, a training corpus, and a switching cost embedded in billions of lines of customer code. Replicating that moat in a three-year window is optimistic. Eroding it at the margin, especially for inference workloads where CUDA lock-in is less severe, is realistic.
AMD's position is best described as a credible, growing, structurally important second source that will not close the gap with Nvidia but will capture enough share to sustain a meaningful GPU revenue line. That description supports a significant Data Center business. It does not support Nvidia-equivalent margins or multiples.