H100 vs H200

H100 vs H200 specifications

Source: NVIDIA H100 and H200 product specifications for SXM form factors. PCIe and NVL versions have different memory, bandwidth and power figures. Full entries: NVIDIA H100 specs and NVIDIA H200 specs.

What the difference means in practice

LLM inference, KV cache and longer context

During inference a GPU must hold the model weights plus a key-value (KV) cache that grows with context length and the number of concurrent requests. Generating each token also means reading weights from memory, so many inference workloads are limited by memory bandwidth rather than raw compute. The H200’s 141 GB leaves more room for KV cache after weights are loaded, which can allow longer contexts or more parallel sequences on one GPU, and its higher bandwidth can help token generation. A model that only just fits on an H100, or needs two H100s, may fit on a single H200. Whether that shows up as higher throughput depends on the serving engine, precision, batching strategy and model.

Training: when memory is or is not the bottleneck

For training jobs where the model, optimizer states and activations already fit comfortably in 80 GB, the H100 and H200 share the same compute architecture, so the gain per GPU may be small. Where memory is the constraint, the H200 can reduce the need for activation checkpointing, aggressive sharding or very small micro-batches, which can simplify the setup and improve how well the GPU is used. Measure on your own training code before assuming either outcome.

Batch size and concurrency

More memory per GPU usually means room for larger batches or more simultaneous users before running out of memory. For serving, that can lower cost per token even if the hourly price is higher, because each GPU handles more work. It is not automatic: latency targets, scheduler behaviour and the model’s attention pattern all affect how much extra concurrency is usable.

Multi-GPU deployment

Both GPUs use fourth-generation NVLink at 900 GB/s and are commonly deployed in eight-GPU HGX servers. Memory is not automatically pooled across GPUs; large models are split with tensor, pipeline or expert parallelism, which adds communication. Fitting a model on fewer H200s can reduce that communication, while very large models still need many GPUs of either type. Because the platforms are similar, existing H100 cluster designs and software generally carry over.

Choose H100 when…

• Your model, context length and batch size fit comfortably in 80 GB per GPU.
• The workload is mainly compute-bound, such as many training jobs on mid-sized models.
• Lower hourly rates or wider availability on your preferred provider matter more than memory headroom.
• You already run H100 clusters and want consistent hardware across jobs.

Choose H200 when…

• Your model plus KV cache exceeds, or sits close to, 80 GB per GPU.
• You serve long contexts or many concurrent users and memory limits your batch size.
• Fitting the model on fewer GPUs would cut parallelism overhead or total GPU count.
• Your own tests show memory bandwidth limits token generation speed.

Not sure which applies? Estimate memory needs with the LLM VRAM calculator, then test a short run on each GPU.

H100 and H200 cloud pricing

Prices below are read from the site’s GPU pricing database at page load, showing only rows with a recorded hourly rate. Rates change often and depend on region, commitment and configuration, so confirm with the provider. Compare more providers on GPU cloud prices and the GPU cloud provider directory.

A higher hourly rate can still mean a lower cost per job if the H200 needs fewer GPUs or serves more requests. Model this with the GPU cloud cost calculator.

Frequently asked questions