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What is Model FLOPs Utilization (MFU)?
Model FLOPs Utilization (MFU) measures how much of a GPU's theoretical compute a job actually uses; goodput counts useful work delivered.
Compute & Pricing LessonsLearning path
One concept connected to AI compute market decisions.
5-8 minutesRead time
A practical introduction designed to be completed in one sitting.
Utilization / Efficiency / TrainingTags
Useful for ml engineers, operators, and analysts.
Plain-English definition
Plain-English definition
Model FLOPs Utilization (MFU) measures how much of a GPU's theoretical compute a training or inference job actually uses for useful model math. Goodput is the related idea of useful work delivered, as opposed to raw throughput. Both reveal how efficiently expensive accelerators are really being used.
Why it matters
Why it matters
A GPU's headline performance is a ceiling that real jobs rarely reach. Low MFU means you are paying for compute you are not using — overhead, waiting on memory or networking, or pipeline stalls eat into it. Improving MFU or goodput lowers the real cost per result without buying more hardware.
- MFU compares useful model math to a GPU's theoretical peak; it is usually well below 100%.
- Goodput counts useful work delivered, not raw throughput.
- Higher MFU and goodput lower cost per result on the same hardware.
Simple example
Simple example
Suppose a training run achieves an illustrative 40% MFU. Improving software, batching, or interconnect to reach an illustrative 50% means the same hardware does about 25% more useful work — cutting cost per result without adding a single GPU.
- A move from 40% to 50% MFU is roughly 25% more useful work on the same hardware.
- Bottlenecks are often memory, networking, or pipeline stalls, not raw compute.
- Treat any MFU percentage as workload- and setup-specific.
Example figures are illustrative calculations, not current quoted market prices.
Market signal
How to read the market signal
Rising MFU and goodput across the industry signal that more output is being squeezed from existing GPUs, which can ease effective demand growth. Watch efficiency gains alongside raw capacity: better utilization can lower cost per result even when chip supply is flat.
- Efficiency gains can lower cost per result without new hardware.
- Persistent low MFU points to memory, networking, or software bottlenecks.
- Utilization metrics separate real efficiency from raw chip counts.
Market read: MFU and goodput reveal how much of paid compute is actually useful. Evidence discipline: state the workload and setup behind any utilization figure, and keep illustrative percentages separate from measured runs.
Common mistake
Common mistake
Reading a GPU's peak performance as what you will get. Real jobs reach a fraction of theoretical FLOPs; ignoring MFU and goodput overstates effective capacity and understates the true cost per result.
Practical takeaway
What you can do with this
Track MFU or goodput, not just GPU count, to find where paid compute is wasted and to lower cost per result.
- Operators and ML engineers: measure MFU and remove memory, networking, and pipeline bottlenecks before buying more GPUs.
- Analysts: read efficiency gains as a lever on effective compute supply and cost.
- Compare cost per useful result, not per raw GPU-hour.
- Treat utilization percentages as workload- and setup-specific.
- Keep measured runs separate from illustrative efficiency targets.
Decision check: before adding GPUs, check whether higher MFU or goodput on existing hardware would meet the need more cheaply.
Helpful memory trick
Helpful memory trick
Peak FLOPs is the speedometer's top number; MFU is how fast you are actually going.
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