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

The previous post argued that utilization, not intelligence, is where the next real constraint in enterprise AI is forming, and it closed by noting that no playbook has emerged yet for what a mature GPU Management practice looks like. This is ours.

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.

The decision, stated precisely

"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.

What FIFO costs under contention

[Figure: side-by-side allocation grids — allocator above, FIFO below, same scenario]