'I paid for the whole GPU, I am going to use the whole GPU': A high-level guide to GPU utilization

GPU utilization can refer to three distinct metrics for neural-network inference workloads: GPU Allocation Utilization, the share of paid GPU time spent running application code; GPU Kernel Utilization, the share spent executing GPU kernels; and Model FLOP/s Utilization (MFU), the share of a GPU’s theoretical arithmetic throughput used for useful model computation. Allocation utilization is constrained by capacity procurement, overprovisioning, provisioning latency, and operational setup, while kernel utilization falls when data transfers, logging, model loading, CPU-side scheduling, or kernel-launch overhead leave GPUs idle. MFU is the most fundamental but difficult measure, since even continuously active GPUs may be limited by inter-GPU communication, memory bandwidth, low arithmetic intensity, or inefficient kernels rather than floating-point capacity. Suggested improvements include faster and more automated provisioning, profiling traces to identify idle CUDA streams and host bottlenecks, batching or CUDA Graphs to reduce scheduling overhead, optimizing communication and memory use, and relying on high-performance libraries such as CuBLAS, PyTorch, and vLLM. The discussion notes that reported allocation utilization is often below 70% at peak demand, that specialized platforms may target over 90% aggregate allocation utilization, and that large-scale training MFU commonly remains far below theoretical hardware limits.

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