A Guide to Understanding GPUs and Maximizing GPU Utilization

2026-04-14 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

Modern AI research, particularly with large-scale models and data, frequently encounters GPU bottlenecks where the CPU struggles to load, preprocess, and transfer data, leaving the GPU idle. This issue, often misattributed to model size, is typically a dataflow problem across the PCIe bridge. GPUs, optimized for parallel operations like matrix multiplication, consist of thousands of cores grouped into Streaming Multiprocessors (SMs) with high-bandwidth VRAM. Key metrics for optimization are VRAM usage and Volatile GPU-Util (compute utilization), with the latter being critical for measuring active instruction execution. The "Roofline Model" formalizes this, showing that most slowdowns are memory-bound rather than compute-bound, characterized by a "sawtooth" GPU utilization graph where the GPU idles between brief spikes of 100% activity.

Key takeaway

For ML researchers and engineers optimizing GPU pipelines, focus on eliminating CPU-GPU bottlenecks by tuning PyTorch `DataLoader` parameters. Implement `num_workers`, `pin_memory=True`, and `prefetch_factor` to ensure continuous data supply. Additionally, adopt mixed precision (BF16/TF32) and leverage `torch.compile()` or Hugging Face `kernels` to maximize compute efficiency and achieve sustained high GPU utilization, transforming idle time into faster experiment cycles.

Key insights

GPU bottlenecks often stem from inefficient data pipelines, not compute, requiring optimized data transfer and processing.

Principles

• Maximize GPU compute utilization, not just VRAM usage.
• Data transfer efficiency is critical for GPU performance.
• Align batch sizes with GPU hardware (multiples of 32 or 64).

Method

Optimize PyTorch DataLoaders by adjusting `num_workers`, enabling `pin_memory=True`, and setting `prefetch_factor`. Enhance GPU compute with larger batch sizes (or gradient accumulation), mixed precision (FP16/BF16/TF32), and kernel fusion via `torch.compile()` or Hugging Face `kernels`.

In practice

• Use `nvidia-smi` or Weights and Biases to monitor GPU-Util.
• Set `num_workers` to 4 and `prefetch_factor` to 2-3.
• Enable `pin_memory=True` for faster data transfer.

Topics

• GPU Utilization
• CPU-GPU Bottleneck
• PyTorch DataLoader Optimization
• Mixed Precision Training
• Kernel Fusion

Code references

• CisMine/Parallel-Computing-Cuda-C
• wandb/wandb
• huggingface/kernels
• huggingface/transformers

Best for: AI Scientist, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.