H200 SXM5 vs V100 SXM2 32GB
NVIDIA H200 SXM5 (Hopper, 141 GB) against NVIDIA V100 SXM2 32GB (Volta, 32 GB): memory, compute, power and rental price, compared for LLM inference and training.
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Side-by-Side Specifications
| Spec | H200 SXM5 | V100 SXM2 32GB |
|---|---|---|
| Architecture | Hopper | Volta |
| Memory | 141 GB HBM3e | 32 GB HBM2 |
| Memory bandwidth | 4,800 GB/s | 900 GB/s |
| FP16 tensor compute | 1,979 TFLOPS | 125 TFLOPS |
| INT8 tensor compute | 3,958 TOPS | 62.8 TOPS |
| Interconnect | NVLink 4.0 · 900 GB/s | NVLink 2.0 · 300 GB/s |
| TDP | 700 W | 300 W |
| Est. on-demand price | ~$10.00/h | ~$2.00/h |
| FP16 TFLOPS per $/h | 198 | 63 |
Highlighted values indicate the stronger spec. Hourly rates are indicative on-demand estimates.
Verdict
Raw performance: The H200 SXM5 leads on FP16 tensor compute (15.8x advantage), which translates directly into higher token throughput for inference and shorter training steps.
Memory: With 141 GB per card, the H200 SXM5 fits larger models on fewer GPUs - fewer cards means less inter-GPU communication and simpler deployments.
Value: At current on-demand rates, the H200 SXM5 delivers more compute per dollar (198 vs 63 FP16 TFLOPS per $/h). If your model fits in its VRAM budget, it is usually the more economical choice.
GPUs Needed for Popular LLMs
Cards required to serve each model at 8-bit quantization (with 20% overhead for activations and KV cache).
| Model | VRAM (8-bit) | H200 SXM5 | V100 SXM2 32GB |
|---|---|---|---|
| GPT-5.6 Sol | 2682 GB | 20x | 84x |
| GLM 5.3 (743B) | 830 GB | 6x | 26x |
| Claude 4.8 Opus (300B) | 335 GB | 3x | 11x |
| Grok 3 Mini | 212 GB | 2x | 7x |
| Qwen 3.6 Plus (110B) | 123 GB | 1x | 4x |
| Nova Core (34B) | 38 GB | 1x | 2x |
| Phi 4 (14B) | 16 GB | 1x | 1x |
| Mage VL | 5 GB | 1x | 1x |
| LFM 2.5 2.6B | 3 GB | 1x | 1x |
Frequently Asked Questions
Which is better for LLM inference: H200 SXM5 or V100 SXM2 32GB?
The H200 SXM5 delivers more raw FP16 compute (1,979 TFLOPS) and the H200 SXM5 offers the most memory per card (141 GB). For cost-efficiency, the H200 SXM5 currently gives more FP16 TFLOPS per dollar of on-demand rental (198 vs 63 TFLOPS per $/h).
How much more memory does the H200 SXM5 have?
The H200 SXM5 has 141 GB of HBM3e versus 32 GB of HBM2 for the V100 SXM2 32GB - a ratio of 4.41x in favor of the H200 SXM5. More VRAM per card means fewer GPUs to fit a given model.
Is the H200 SXM5 or the V100 SXM2 32GB cheaper to rent?
Estimated on-demand rates are ~$10.00/h for the H200 SXM5 and ~$2.00/h for the V100 SXM2 32GB. Raw hourly price is only part of the story: normalize by throughput (TFLOPS per $/h) and by how many cards you need for your model's VRAM.
How do the H200 SXM5 and V100 SXM2 32GB compare on power?
The H200 SXM5 has a TDP of 700W versus 300W for the V100 SXM2 32GB. FP16 compute per watt: 2.8 vs 0.4 TFLOPS/W.
Deploy on a GPU cloud
Rent the H200 SXM5 or V100 SXM2 32GB by the hour instead of buying hardware.
