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A performance metric indicating the percentage of time a GPU actively executes kernels or workloads during a sampling interval.
DESCRIPTION
GPU utilization measures active compute use over time and indicates if kernels saturate the device. Low utilization often signals memory bottlenecks, small batch sizes, or I/O stalls. Monitoring utilization guides tuning of input pipelines, kernel fusion, and batch sizing. It is tracked with tools like nvidia-smi, DCGM, and cloud monitoring. Sustained high utilization should correlate with throughput metrics for healthy training or inference.
ALTERNATIVE DEFINITIONS
NVIDIA
utilization.gpu [%]: The percentage of time over the past sample period during which kernels were executing on the GPU.
Defines utilization precisely over a sampling period tied to kernel execution.
NVIDIA DCGM
GPU utilization indicates active processing time of the GPU and streaming multiprocessors, used to assess workload saturation and performance.
Connects utilization to SM activity and workload saturation.
AWS CloudWatch
GPUUtilization reports the percentage of time the GPU was busy processing over the sample period.
Cloud monitoring metric definition aligning with utilization semantics.
RELATED TERMS
nvidia-smi DCGM SM Occupancy CUDA Kernels Throughput VRAM Mixed Precision Training Batch Size Profiling Bottleneck
COMPETING TERMS
CPU Utilization
KNOWLEDGE AREAS
Observability & Telemetry Infrastructure MLOps
TARGET AUDIENCE
MLOps Engineer AI Engineer DevOps Engineer Hardware Engineer
EXTERNAL SOURCES
- Wikipedia - Graphics processing unit
- NVIDIA - nvidia-smi usage and metrics
- NVIDIA DCGM - Data center GPU management guide
- Google Cloud - GPU metrics collection guide
TOP QUESTIONS
What indicates low GPU Utilization during training?
I/O or data loader bottlenecks, small batch sizes, or memory-bound kernels usually cause low utilization.
Category: Example
How to increase GPU Utilization?
Increase batch size, optimize input pipelines, fuse kernels, and reduce CPU-side overheads.
Category: HowTo
What is a common misunderstanding about GPU Utilization?
High utilization alone doesn’t guarantee throughput; measure iterations per second and latency as well.
Category: Misunderstanding
What risks exist when chasing 100% GPU Utilization?
Overlarge batches can degrade generalization or cause out-of-memory; balance utilization with stability and accuracy.
Category: Risks
How is GPU Utilization measured?
Tools like nvidia-smi and DCGM sample active kernel time over intervals, reporting utilization percentages.
Category: Example
Does mixed precision affect GPU Utilization?
Often yes; faster kernels and reduced memory traffic increase utilization and throughput.
Category: Opinion
What is a hot topic around GPU Utilization?
Pipeline parallelism and data streaming for LLMs to keep GPUs saturated across multi-node training.
Category: Topic
How does VRAM relate to GPU Utilization?
Insufficient VRAM forces small batches and idle compute; adequate memory helps sustain high utilization.
Category: Example
How can cloud monitoring help GPU Utilization?
Cloud metrics and alerts detect underutilization and guide autoscaling or pipeline optimization.
Category: Management