H100 vs H200 vs B300: which GPU should you rent?
Published
Rent H100 when your workload fits in 80 GB per GPU and you want the widest choice of suppliers. Test H200 (141 GB HBM3E) when extra memory simplifies sharding or longer contexts. Evaluate B300 (Blackwell Ultra) for the largest memory and multi-node scaling requirements.
Start with a workload that runs, a result you can measure and a budget for the complete job. The right rental is the configuration that meets those requirements at an acceptable total cost. A newer GPU does not settle the decision by itself.
Compare the exact variants
| Candidate | Useful starting fact | What to establish before choosing |
|---|---|---|
| H100 SXM | NVIDIA specifies 80 GB of GPU memory | Does the workload fit with realistic runtime headroom? |
| H200 SXM | NVIDIA specifies 141 GB of HBM3E memory | Does extra memory reduce sharding or support the needed batch and context? |
| B300-based system | Blackwell Ultra provides more per-GPU memory than these Hopper configurations | Which platform, exposed memory, software stack and cluster layout does the supplier offer? |
The H100 name also covers other variants. NVIDIA lists H100 NVL at 94 GB per GPU, so an H100 NVL offer should not be silently treated as an H100 SXM offer. Compare the actual SKU and topology. NVIDIA H100 specifications · NVIDIA H200 specifications · NVIDIA Blackwell Ultra specifications
First, check memory fit
For training, account for the model, activations, optimizer state and the framework’s working memory. For self-managed serving, account for model weights, the KV cache and the concurrency and context lengths you intend to support.
Measure with a representative configuration rather than dividing model size by advertised VRAM. Sharding a workload across more GPUs introduces communication requirements. Extra memory may let you change the layout, but the benefit needs to appear in an end-to-end test.
Then check the software and system
Confirm the framework, CUDA, driver and communication-library versions supported by the supplier. Record precision settings and any model changes made for the test. A result obtained with a different quality target or batch size is not a clean comparison.
For multi-node training, evaluate the network alongside the GPU. NVIDIA’s NCCL guidance separates intra-node connectivity from inter-node performance and recommends checking the actual communication paths. A GPU model name cannot tell you whether a supplier’s fabric is suitable. NVIDIA NCCL performance guidance
Compare completed work per total spend
Ask each supplier to quote a configuration that can meet the same workload target. Include every allocated GPU, the billed duration, storage, data transfer and mandatory services. Add the cost of any minimum commitment that your workload will not use.
For training, compare elapsed time to the same agreed result. For serving, compare sustainable throughput at the same latency and quality requirements. Include setup, data movement and restart overhead where relevant.
A higher hourly rate can still produce a cheaper completed job. A larger cluster can also cost more without finishing proportionally faster. Measure both possibilities.
A practical shortlist
- Include H100 SXM when your workload already fits and you want a measured baseline
- Test H200 when additional memory could simplify the configuration
- Test B300 when memory, supported low-precision execution or scaling requirements justify evaluating Blackwell Ultra
These are evaluation paths, not performance guarantees. Availability and commercial terms may change the shortlist.
Send RackShore the workload, required GPU count, region, timing and acceptable alternatives. We match buyers with suppliers; the selected supplier contracts, bills and supports the rental directly.
Have a requirement in mind?
Send a nonbinding brief. Suppliers confirm availability and terms.