H100 80GB SXM vs NVIDIA H200 141GB for Local LLMs
The NVIDIA H200 141GB holds bigger models (141 GB vs 80.0 GB), and the NVIDIA H200 141GB generates tokens about 37% faster. Below: speeds on seven popular models, the models only one card can run, and our verdict.
Specs that matter for AI
| H100 80GB SXM | NVIDIA H200 141GB | |
|---|---|---|
| Memory | 80 GB | 141 GB |
| GPU-usable memory | 80.0 GB | 141 GB |
| Bandwidth | 3350 GB/s | 4800 GB/s |
| Architecture | Hopper | Hopper |
| Released | 2022-09 | 2024-11 |
| Llama 3.1 8B Q4_K_M | ~357 tok/s | ~487 tok/s |
Speed and fit by model
Q4_K_M (or native format) with 8K context.
| Model | VRAM | H100 80GB SXM | NVIDIA H200 141GB |
|---|---|---|---|
| Llama 3.1 8B | 6.3 GB | Fits ~357 tok/s | Fits ~487 tok/s |
| Qwen 3 14B | 10.5 GB | Fits ~207 tok/s | Fits ~286 tok/s |
| gpt-oss-20b | 13.9 GB | Fits ~283 tok/s | Fits ~313 tok/s |
| Qwen 3 30B-A3B | 18.8 GB | Fits ~169 tok/s | Fits ~180 tok/s |
| Qwen 3 32B | 21.4 GB | Fits ~97 tok/s | Fits ~136 tok/s |
| Llama 3.3 70B | 43.2 GB | Fits ~47 tok/s | Fits ~67 tok/s |
| gpt-oss-120b | 62.0 GB | Fits ~201 tok/s | Fits ~219 tok/s |
Verdict
Same Hopper compute, but the H200 has 141 GB of HBM3e and about 43% more bandwidth (4,800 vs 3,350 GB/s). That means faster generation, bigger batches and longer contexts per card, and models like Qwen 3 235B fit on one H200 at Q4. When renting, the H200 is usually the better value per token for large models.
Which should you buy?
Choose the H100 80GB SXM if…
- Your models fit in 80 GB with your batch size.
- You rent and the hourly price is lower.
- You already have H100 infrastructure.
Choose the NVIDIA H200 141GB if…
- You serve large models or long contexts.
- You want fewer GPUs per model.
- You care about throughput per card.
Best quality and longest context on each card
The highest-quality quantization that fits fully at 8K context, and the longest context that fits at Q4_K_M with an FP16 cache.
| Model | H100 80GB SXM: best quant | NVIDIA H200 141GB: best quant | H100 80GB SXM: max context | NVIDIA H200 141GB: max context |
|---|---|---|---|---|
| Qwen 3.5 9B | FP16 / BF16 | FP16 / BF16 | 256K | 256K |
| Qwen 3 14B | FP16 / BF16 | FP16 / BF16 | 40K | 40K |
| gpt-oss-20b | MXFP4 (official) | MXFP4 (official) | 128K | 128K |
| Qwen 3.6 27B | FP16 / BF16 | FP16 / BF16 | 256K | 256K |
| Qwen 3 32B | FP16 / BF16 | FP16 / BF16 | 40K | 40K |
| Llama 3.3 70B | Q8_0 | Q8_0 | 110K | 128K |
Image generation
Best weight precision for image models with model swapping, at each model's default resolution. Image generation is also limited by compute, so the card with more tensor-core throughput finishes each image faster even when both fit.
| Model | H100 80GB SXM | NVIDIA H200 141GB |
|---|---|---|
| SDXL 1.0 | FP16 / BF16 | FP16 / BF16 |
| FLUX.1 [dev] | FP16 / BF16 | FP16 / BF16 |
| Qwen-Image | FP16 / BF16 | FP16 / BF16 |
| Wan 2.2 A14B (T2V / I2V) | FP16 / BF16 | FP16 / BF16 |
Models only one card can run
Only H100 80GB SXM
None
Only NVIDIA H200 141GB
Frequently asked questions
Is the H100 80GB SXM or NVIDIA H200 141GB better for local LLMs?
Same Hopper compute, but the H200 has 141 GB of HBM3e and about 43% more bandwidth (4,800 vs 3,350 GB/s). That means faster generation, bigger batches and longer contexts per card, and models like Qwen 3 235B fit on one H200 at Q4. When renting, the H200 is usually the better value per token for large models.
Which is faster for LLM inference?
The NVIDIA H200 141GB, by about 37% for token generation on models that fit both (about 487 vs 357 tokens/s on Llama 3.1 8B Q4_K_M).
Which can run bigger models?
The NVIDIA H200 141GB, with 141 GB usable versus 80.0 GB.