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H100 vs H200 vs B200 vs B300: Which GPU Should You Rent?

We compare NVIDIA's data center GPUs by memory, bandwidth and use case — a practical guide to choosing the right GPU for your project.

CWCHost Team2 min read

Choosing a GPU for an AI project directly affects your budget and timeline. In this post we compare NVIDIA's four most in-demand data center GPUs from the perspective that matters when renting.

Quick comparison

| GPU | Architecture | Memory | Bandwidth | NVLink |
| --- | --- | --- | --- | --- |
| H100 SXM | Hopper | 80 GB HBM3 | 3.35 TB/s | 900 GB/s |
| H200 SXM | Hopper | 141 GB HBM3e | 4.8 TB/s | 900 GB/s |
| B200 | Blackwell | 180 GB HBM3e | 8 TB/s | 1.8 TB/s |
| B300 | Blackwell Ultra | 288 GB HBM3e | 8 TB/s | 1.8 TB/s |

Why memory matters so much

With large language models, the bottleneck is usually memory, not compute. The BF16 weights of a 70-billion-parameter model take roughly 140 GB. That model:

  • must be split across at least two GPUs on H100,
  • fits on a single H200, but leaves little room for the KV cache,
  • runs on a single B200 or B300 with plenty of KV cache headroom for inference.

Fewer shards mean less communication overhead and simpler infrastructure.

Which one for which workload?

H100: Proven and ubiquitous

The GPU with the most mature ecosystem. A safe choice for fine-tuning, mid-scale training and production inference.

H200: Memory-bound inference

Same compute as H100, but significantly more memory and bandwidth. It can be more efficient than H100 for long-context applications and serving 70B-class models.

B200: Next-generation training

The second-generation Transformer Engine with FP4 and FP8 delivers major speed-ups for large-model training and high-throughput inference.

B300: Top-tier scale

Designed for reasoning models, very long context windows and trillion-parameter-scale models. 288 GB per GPU means more than 2.3 TB of memory in a single 8-GPU server.

Three questions to get it right

  1. How many parameters, and at what precision? This sets your minimum memory requirement.
  2. Training or inference? Training is more sensitive to GPU-to-GPU interconnect (NVLink); inference to memory bandwidth.
  3. For how long? Flexibility matters for projects lasting a few months; contract terms matter for long-running projects.

Not sure yet? Select "Not sure" on the quote form and we'll work out the right configuration for your workload together.

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