AI inference is projected to grow from roughly 20% of AI workloads in 2025 to about 80% by 2030, shifting infrastructure constraints from GPUs to power and capacity utilization. For most providers, the fastest path to support this surge is segmenting their workloads and aligning different workload types with infrastructure that is optimized for each type. For latency-tolerant workloads, a growing segment of AI inference, unlocking stranded capacity inside existing data centers offers the fastest path to deployment. In a joint PoC, Hammerhead’s orchestration platform, ORCA, integrated with Dheyo’s GPU and workload optimization layer safely added 30% more effective GPU capacity under the same power allocation. This solution delivered an increase in completed text-to-video inference jobs by 18–20%, with only a 10–25% latency increase. The core implication is that AI inference companies can expand capacity in 6–9 months using existing facilities rather than waiting for the grid, thus turning stranded watts into a repeatable competitive advantage.