AI Infrastructure by Workload

How Hardware Varies by Workload

Choosing a GPU cloud provider is rarely a one-size-fits-all decision. The underlying hardware requirements shift drastically depending on whether you are training a foundation model from scratch, fine-tuning an existing open-source model, or serving millions of inference requests.

For Training (LLMs & Vision): The bottleneck is almost always memory bandwidth and network interconnectivity. You require highly networked clusters (like NVLink and InfiniBand) using flagship silicon like the NVIDIA H100.

For Inference & Rendering: Throughput and cost-per-token become the primary metrics. Providers offering scalable API endpoints powered by NVIDIA L40S, L4, or RTX 4090 instances typically offer the best ROI.

Frequently Asked Questions