GPUs accelerate ML training and inference, video encoding, and rendering. Not all providers offer GPU; check availability and drivers. Cost is higher; size the workload and use spot or preemptible if available.
When to use GPU
- ML training: Training large models (vision, NLP, etc.) is much faster on GPU. Need CUDA (NVIDIA) or ROCm (AMD) and framework support (PyTorch, TensorFlow).
- ML inference: Serving models at scale can use GPU for low latency and throughput. Not all inference needs GPU; measure CPU vs GPU for your model.
- Video encoding: Transcoding and encoding (e.g. H.264, HEVC) are GPU-accelerated with NVENC and similar. Reduces CPU load and time.
- Rendering: 3D and VFX rendering; scientific simulation. GPU can dramatically speed up parallel workloads.
Provider and sizing
- Availability: Not every host has GPU instances; check region and instance type. Often limited inventory.
- Drivers and stack: Ensure OS and drivers (NVIDIA, AMD) are supported. Some providers offer pre-built ML images.
- Cost: GPU instances are expensive. Use spot or preemptible for batch training if your workload tolerates interruption; reserve for production inference if needed.
Best practices
- Size right: Start with one GPU and scale; avoid over-provisioning. Monitor utilization.
- Data locality: Keep training data close (same region or fast link) to avoid transfer cost and latency.
- Persistent storage: Training data and checkpoints on fast storage (SSD, NVMe); GPU is useless if I/O is the bottleneck.
Summary
Use GPU for ML training/inference, video encoding, and rendering when the workload benefits. Check provider availability and drivers; size and consider spot for cost. Keep data and storage in mind.
