CPU vs H100

The H100 is generally better for large language model training due to its higher throughput and 80 GB of VRAM, which allows fitting larger models or larger batch sizes in a single pass. For smaller models or fine-tuning tasks where cost matters more, both GPUs can be effective.

Pricing for the CPU and H100 varies by cloud provider, region, and contract type. Shadeform aggregates pricing from 30+ GPU cloud providers so you can compare and find the best rate. Use the instance table above to see current on-demand prices.

The H100 has more VRAM at 80 GB, compared to 0 GB on the CPU. Higher VRAM allows you to run larger models without quantization, use longer context windows, and process larger batch sizes — all of which improve throughput and reduce latency for memory-bound workloads.

The H100 is currently available across 13 cloud providers on Shadeform's network, compared to 2 for the CPU. Shadeform lets you deploy either GPU across all available providers from a single platform, so you can always find available capacity without manually checking each cloud.

Mixing different GPU types in a single training cluster is generally not recommended, as it creates performance bottlenecks where faster GPUs wait for slower ones. For best results, use a homogeneous cluster of either CPU or H100. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.