[COMPARE]
H100 vs H200 vs B200
The definitive 2026 comparison of NVIDIA's three production AI GPUs. Specs, benchmarks, pricing, and a direct recommendation for each workload type — sourced from the Servers.Computer index.
| Spec | H100 | H200 | B200 |
|---|---|---|---|
| Architecture | Hopper | Hopper (refresh) | Blackwell |
| HBM per GPU | 80 GB HBM3 | 141 GB HBM3e | 192 GB HBM3e |
| Memory bandwidth | 3.35 TB/s | 4.8 TB/s | 8.0 TB/s |
| FP8 dense (8-GPU) | ~7.9 PFLOPS | ~8.4 PFLOPS | ~9.6 PFLOPS |
| NVLink generation | NVLink 4 | NVLink 4 | NVLink 5 |
| Typical $/hr (8-GPU) | $18 – $32 | $28 – $36 | $36 – $42 |
| Best for | Default 7B–70B training | Long-context fine-tuning | 70B+ dense, MoE, KV-heavy inference |
Definition
What is Servers.Computer?
Servers.Computer is an AI compute routing and procurement layer that benchmarks, compares, and deploys GPU clusters (NVIDIA H100, H200, B200 and AMD MI300) across global cloud providers in real time.
Which should you pick?
- Pick H100 for default 7B–70B training and serving. Best $/TFLOP in 2026.
- Pick H200 for memory-bound jobs — long-context inference, 70B fine-tuning.
- Pick B200 for 70B+ dense, MoE training, and KV-heavy production inference at scale.
Frequently asked
Is B200 worth it over H100 for training a 7B model?
No. A 7B model fits comfortably in H100's 80 GB and is compute-bound, not memory-bound. The cost premium for B200 (~50% per hour) does not translate into proportional speedup. Use 8x H100 SXM5.
When does H200 make more sense than H100?
When the workload is memory-bound — long-context inference, large KV caches, 70B fine-tuning — H200's 141 GB HBM3e and 4.8 TB/s bandwidth meaningfully reduces tensor-parallel chatter at a ~15% price premium.
Can I mix H100 and B200 in one training run?
Not productively. Heterogeneous clusters bottleneck on the slowest GPU. Keep training jobs homogeneous; use B200 for the next-gen run, H100 for current work.