Allocation Strategy Increases GPU Utilization by 33 Percentage Points

The core constraint for enterprise AI is not model intelligence but GPU Utilization. This article shares results from building a Constraint-aware GPU allocator to replace the existing FIFO (First-In-First-Out) scheduler, achieving innovative performance improvements.

Testing under identical hardware and workload conditions showed that GPU utilization increased by up to 33 percentage points compared to the FIFO method, and Priority-weighted output increased by up to 105%. This result was achieved solely by optimizing the Order of allocation without changing the hardware.

Key Improvement Principles:

• Resolving Reservation Cost: The FIFO method, which constantly reserves GPUs for Real-time inference, fails to utilize idle GPUs during non-peak times for batch jobs. The new allocator manages this efficiently.
• Strategic Allocation Order: While FIFO simply allocates based on arrival order, hindering high-value tasks, the new approach treats the allocation order itself as a key factor in capacity determination to find the optimal combination.

In environments with mixed workloads such as training, real-time inference, batch inference, and quantization, it was proven that maximizing the cluster's total capacity is possible solely through optimizing the allocation order.