The NVIDIA H200 is an enterprise-class GPU built on the Hopper architecture, featuring 141 GB of memory with 4,800 GB/s of memory bandwidth and a 700 W TDP. It delivers 67 TFLOPS of FP32 performance and 989 TFLOPS of FP16 tensor performance. Currently available from 6 providers starting at $3.29/GPU/hour, with a market median of $4.00/GPU/hour across 22 configurations.
Hardware specifications
Same across all providers
Median price across all providers
8 offerings from 6 providers
Sorted by price ascending. Compare configurations side-by-side.
| Provider | Count | vCPU | RAM | Region | Per GPU hour | Total/hr | Action |
|---|---|---|---|---|---|---|---|
| ×1 | 12 | 188 GB | $4.590 | $4.59 | Launch | ||
Sesterce CloudReferral link | ×8 | 96 | 2 TB | -- | From$2.475 -4.4% 30d | From$19.80 | |
| ×8 | 190 | 1.76 TB | -- | $2.720 | $21.76 | ||
| ×1 | 24 | 236 GB | From$3.290 -5.0% 30d | From$3.29 | Launch | ||
Theta EdgeCloudTrending | ×1 | 8 | 141 GB | -- | $3.690 0.0% 30d | From$3.69 | Launch |
OblivusReferral link | ×1 | — | — | $3.990 | $3.99 | Launch | |
| ×8 | 176 | 1.76 TB | $3.990 0.0% 30d | $31.92 | Launch | ||
| ×1 | 44 | 170 GB | -- | $4.000 | From$4.00 | Launch | |
Up to$500credit | ×1 | 12 | 188 GB | $4.590 0.0% 30d | $4.59 | Launch |
LLMs that fit on the NVIDIA H200
Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 141 GB per GPU. Showing the 12 largest that fit.
| Model | Parameters | 16-bit | 8-bit | 4-bit |
|---|---|---|---|---|
| DeepSeek V4 Pro DeepSeek | 1.6T 49B active | — Not released | — Not released | 1,038 GB 8× · 90 GB free |
| Kimi K2.5 Moonshot AI | 1T 32B active | — Not released | — Not released | 714 GB 8× · 414 GB free |
| GLM-5.2 Z.ai | 753B | 1,808 GB Too large | 994 GB 8× · 134 GB free | 579 GB 8× · 549 GB free or 4× · 16% spare |
| DeepSeek R1 DeepSeek | 671B 37B active | — Not released | 826 GB 8× · 302 GB free | 481 GB 4× · 83 GB free |
| DeepSeek V4 Flash DeepSeek | 284B | — Not released | — Not released | 192 GB 2× · 90 GB free |
| Solar Open2 250B Upstage | 250B 15B active | 601 GB 8× · 527 GB free or 4× · 12% spare | 330 GB 4× · 234 GB free or 2× · 2% spare | 192 GB 2× · 90 GB free |
| Qwen3 235B-A22B Alibaba | 235B 22B active | 564 GB 8× · 564 GB free or 4× · 19% spare | 310 GB 4× · 254 GB free or 2× · 9% spare | 181 GB 2× · 101 GB free |
| Laguna-S 2.1 Poolside | 118B 8B active | 282 GB 4× · 282 GB free or 2× · 19% spare | 155 GB 2× · 127 GB free or 1× · 9% spare | 90 GB 1× · 51 GB free |
| gpt-oss-120b OpenAI | 117B 5.1B active | — Not released | — Not released | 78 GB 1× · 63 GB free |
| Qwen3.6 35B-A3B Alibaba | 35B 3B active | 86 GB 1× · 55 GB free | 47 GB 1× · 94 GB free | 28 GB 1× · 113 GB free |
| Qwen3 32B Alibaba | 32.8B | 79 GB 1× · 62 GB free | 43 GB 1× · 98 GB free | 25 GB 1× · 116 GB free |
| Gemma 4 31B Google | 31.3B | 75 GB 1× · 66 GB free | 41 GB 1× · 100 GB free | 24 GB 1× · 117 GB free |
Estimates: model weights plus 20% for KV cache and runtime overhead, at short context lengths. Long contexts and large batches need more. Rows marked “tight” hold the weights but not that full margin. A ×N figure is the total VRAM across N of these GPUs and makes no claim about interconnect throughput. How we estimate this · Start from a model instead
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