NVIDIA H100
The NVIDIA H100 is an enterprise-class GPU built on the Hopper architecture, featuring 80 GB of memory with 3,350 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 15 providers starting at $1.49/GPU/hour, with a market median of $3.20/GPU/hour across 89 configurations.
Hardware specifications
Same across all providers
Median price across all providers
16 offerings from 15 providers
Sorted by price ascending. Compare configurations side-by-side.
| Provider | Count | vCPU | RAM | Region | Per GPU hour | Total/hr | Action |
|---|---|---|---|---|---|---|---|
| ×1 | 8 | 125 GB | $3.290 | $3.29 | Launch | ||
| ×1 | 26 | 221 GB | From$1.490 -4.3% 30d | From$1.49 | Launch | ||
Theta EdgeCloudTrending | ×1 | 10 | 80 GB | -- | $2.290 | From$2.29 | Launch |
| ×1 | 28 | 180 GB | From$2.500 +14.0% 30d | From$2.50 | Launch | ||
| ×1 | 28 | 180 GB | From$2.630 0.0% 30d | From$2.63 | Launch | ||
| ×1 | 28 | 180 GB | $2.750 0.0% 30d | From$2.75 | Launch | ||
| ×1 | 20 | 128 GB | -- | From$3.000 | From$3.00 | ||
Thunder ComputeReferral link | ×1 | 18 | 90 GB | -- | $3.190 | From$3.19 | Launch |
| ×1 | 30 | 120 GB | -- | $3.250 | From$3.25 | Launch | |
| ×1 | 26 | 200 GB | From$3.290 -1.2% 30d | From$3.29 | Launch | ||
Up to$500credit | ×1 | 8 | 125 GB | From$3.290 -6.1% 30d | From$3.29 | Launch | |
Up to$1credit | ×8 | 128 | 1.5 TB | From$3.334 +0.9% 30d | From$26.67 | Launch | |
| ×1 | 24 | 240 GB | From$3.354 +2.8% 30d | From$3.35 | Launch | ||
Novita AIReferral link | ×1 | 16 | 128 GB | $3.390 0.0% 30d | From$3.39 | Launch | |
OblivusReferral link | ×1 | 28 | 180 GB | From$3.500 0.0% 30d | From$3.50 | Launch | |
Sesterce CloudReferral link | ×2 | 48 | 480 GB | From$3.630 +25.5% 30d | From$7.26 | Launch | |
| ×1 | 20 | 240 GB | $4.410 +30.1% 30d | From$4.41 | Launch |
LLMs that fit on the NVIDIA H100
Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 80 GB per GPU. Showing the 12 largest that fit.
| Model | Parameters | 16-bit | 8-bit | 4-bit |
|---|---|---|---|---|
| Kimi K2.5 Moonshot AI | 1T 32B active | — Not released | — Not released | 714 GB Tight on 8× · 7% spare |
| GLM-5.2 Z.ai | 753B | — Not released | 994 GB Too large | 579 GB 8× · 61 GB free |
| DeepSeek R1 DeepSeek | 671B 37B active | — Not released | 826 GB Too large | 481 GB 8× · 159 GB free |
| DeepSeek V4 Flash DeepSeek | 284B | — Not released | — Not released | 192 GB 4× · 128 GB free or 2× · 0% spare |
| Solar Open2 250B Upstage | 250B 15B active | 601 GB 8× · 39 GB free | 330 GB 8× · 310 GB free or 4× · 16% spare | 192 GB 4× · 128 GB free |
| Qwen3 235B-A22B Alibaba | 235B 22B active | 564 GB 8× · 76 GB free | 310 GB 4× · 9.7 GB free | 181 GB 4× · 139 GB free or 2× · 6% spare |
| Laguna-S 2.1 Poolside | 118B 8B active | 282 GB 4× · 38 GB free | 155 GB 2× · 4.8 GB free | 90 GB 2× · 70 GB free or 1× · 6% spare |
| gpt-oss-120b OpenAI | 117B 5.1B active | — Not released | — Not released | 78 GB 1× · 1.7 GB free |
| Qwen3.6 35B-A3B Alibaba | 35B 3B active | 86 GB 2× · 74 GB free or 1× · 11% spare | 47 GB 1× · 33 GB free | 28 GB 1× · 52 GB free |
| Qwen3 32B Alibaba | 32.8B | 79 GB 1× · 1.4 GB free | 43 GB 1× · 37 GB free | 25 GB 1× · 55 GB free |
| Gemma 4 31B Google | 31.3B | 75 GB 1× · 4.9 GB free | 41 GB 1× · 39 GB free | 24 GB 1× · 56 GB free |
| GLM-4.7 Flash Z.ai | 31.2B | 75 GB 1× · 5.1 GB free | 41 GB 1× · 39 GB free | 24 GB 1× · 56 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
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