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

We built a constraint-aware GPU allocator and benchmarked it against a FIFO scheduler across seven benchmark scenarios. On identical hardware, running identical workloads, GPU utilization rose by as much as 33 percentage points, and priority-weighted output rose in every one of them, by as much as 105%. Nothing about the hardware changed. What changed was the order in which allocation decisions get made.

One note on measurement before the numbers start. Every gain below is expressed as improvement over the FIFO result on the same scenario. Utilization is reported in percentage points; value is reported as a percentage increase in priority-weighted output.

"Keep the GPUs busy" is not a decision a system can execute. The decision is narrower and much harder: which GPU runs which job, in which timestep, at what priority. Formally it is one binary choice per combination of GPU, job and timestep, and the output is a grid — every GPU, across the whole scheduling horizon, with a job name in each cell or nothing at all.

Four workload types compete for that grid: training, real-time inference, batch inference, and quantization. They split into two allocation shapes, and the split is where the difficulty lives. Training, batch inference and quantization are batch-like: once started, each needs a contiguous block of GPUs held without interruption until the job finishes. Real-time inference is the opposite: elastic, driven by a demand curve that changes every timestep, growing and shrinking as traffic does.

Two incompatible shapes competing for the same hardware in the same timestep is the core problem. A second heterogeneity sits inside a single type: for the same base model, training jobs range from a few hours to several days, and from one GPU to dozens.

The comparison point throughout is a FIFO-based scheduler: real-time inference served from a fixed reservation, and every other job placed in arrival order, without regard for priority.