The future of AI needs more flexible GPU capacity

Generative AI’s rapid growth has intensified demand for constrained GPU supplies, with training currently consuming most capacity despite inference being the primary path to revenue. Inference workloads are especially difficult to provision because request volumes fluctuate across seconds, daily and weekly cycles, unexpected events, and long-term growth patterns, forcing companies with fixed GPU reservations to maintain excess capacity and often accept low utilization. Training, fine-tuning, batch jobs, and other GPU tasks can also be variable, making flexible access valuable beyond inference. The proposed alternative is a more on-demand, multi-tenant GPU market that pools demand across customers and supply across regions, GPU generations, and cloud vendors, while using rapid container and model initialization to scale workloads quickly. Demand smoothing, such as scheduling latency-tolerant work during off-peak periods, could further improve efficiency. The outlook presented is that inference will account for a growing share of GPU consumption, on-demand capacity will become more common for inference and smaller training workloads, and long-term reservations will remain useful for the lowest prices but cease to be the default model.

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