Same Cluster, 33 Points More Utilization: What Changed Was the Order

Details

Dharma-AI published a detailed benchmarking report comparing their constraint-aware GPU allocator against a standard FIFO scheduler across seven scenarios on identical hardware. The key insight: FIFO's ordering cost is invisible when clusters have slack, but becomes expensive under contention, when training jobs, batch inference, real-time inference, and quantization compete for the same GPUs. The constraint-aware allocator solves a binary scheduling problem — which GPU runs which job at which timestep — by handling two incompatible allocation shapes: batch-like jobs (training, batch inference, quantization) that need contiguous GPU blocks held to completion, and elastic real-time inference that scales with traffic demand. FIFO ignores this shape mismatch. Across seven benchmark scenarios, the new allocator raised GPU utilization by up to 33 percentage points and priority-weighted output by up to 105%. The report covers measurement methodology and notes that gains are expressed relative to FIFO baselines on the same scenario — a transparent framing that makes the numbers credible. No hardware changes were required. For enterprise AI teams managing shared GPU clusters, this is a practical demonstration that scheduling policy itself is a significant optimization lever. The full benchmark breakdown by workload type and scenario is available on Hugging Face.