How do you test GPU utilization in model inference?

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🔹 1. Monitor GPU in Real-Time

• NVIDIA tools:

  • nvidia-smi → Shows GPU utilization (%), memory usage, temperature, and running processes.

  • Example metrics:

    • GPU Utilization (%) → How busy the GPU cores are.

    • Memory Usage (MB/GB) → How much VRAM is allocated by your model.

    • Power Usage (W) → Helps check efficiency.

• Windows/Linux GUI tools:

  • NVIDIA System Management Interface

  • GPU-Z (Windows) or nvtop (Linux).

🔹 2. Profiling Framework Tools

• TensorFlow: Use TensorBoard Profiler → shows GPU utilization, memory bandwidth, kernel execution times.

• PyTorch: Use torch.profiler to track GPU activity and execution timeline.

• ONNX Runtime: Provides execution profiling for inference latency per operator.

🔹 3. Benchmark Inference Workload

• Run inference with different batch sizes and input dimensions.

• Measure:

  • Latency (time per inference).

  • Throughput (inferences per second).

  • GPU utilization (%).

• This helps check if the GPU is underutilized (e.g., only 10–20%) or saturated (>90%).

🔹 4. System-Level Monitoring

• Use Prometheus + Grafana dashboards for continuous monitoring in production.

• Collect metrics: GPU utilization, memory, temperature, and inference latency.

🔹 5. Optimization Tests

If utilization is low, test optimizations:

• Increase batch size.

• Use mixed precision (FP16/INT8).

• Optimize model with TensorRT or ONNX Runtime.

• Pin model execution to GPU vs CPU fallback.

✅ In short:
To test GPU utilization during inference, use nvidia-smi or profiling tools (TensorBoard, PyTorch profiler), track metrics like utilization %, memory, and latency, and run benchmarks with varying loads.

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