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The article argues that as local AI models improve, the economic case for buying hardware weakens because rented models also advance, leading to lower utilization and fixed depreciation costs; buying is justified only for data privacy or high-utilization scenarios.
This article presents a constraint-aware GPU allocator that improves GPU utilization by up to 33 percentage points compared to FIFO scheduling, demonstrating the critical role of priority-based allocation in enterprise AI systems.
The article argues that GPU utilization is becoming the key constraint in enterprise AI, analogous to aircraft utilization in aviation, and that idle GPUs represent wasted capacity that determines competitive advantage.
A new paper from Google introduces GQM, which increased node occupancy from below 75% to over 93%, significantly reducing compute wastage at scale.