NVIDIA B200
The NVIDIA B200 is an enterprise-class GPU built on the Blackwell architecture, featuring 180 GB of memory with 8,000 GB/s of memory bandwidth and a 1,000 W TDP. It delivers 75 TFLOPS of FP32 performance and 2,250 TFLOPS of FP16 tensor performance. Currently available from 5 providers starting at $5.10/GPU/hour, with a market median of $6.11/GPU/hour across 13 configurations.
Hardware specifications
Same across all providers
Median price across all providers
7 offerings from 5 providers
Sorted by price ascending. Compare configurations side-by-side.
| Provider | Count | vCPU | RAM | Region | Per GPU hour | Total/hr | Action |
|---|---|---|---|---|---|---|---|
| ×1 | 24 | 188 GB | $6.790 | $6.79 | Launch | ||
Sesterce CloudReferral link | ×8 | 96 | 2 TB | -- | From$4.114 0.0% 30d | From$32.91 | |
Beyond.plCheapest | ×1 | 28 | 256 GB | $5.100 | From$5.10 | Launch | |
| ×1 | 24 | 250 GB | From$5.310 +6.6% 30d | From$5.31 | Launch | ||
| ×8 | 252 | 2 TB | $6.000 | $48.00 | Launch | ||
| ×1 | 30 | 170 GB | -- | $6.110 | From$6.11 | Launch | |
| ×8 | 208 | 2.83 TB | -- | From$6.690 | From$53.52 | ||
Up to$500credit | ×1 | 24 | 188 GB | $6.790 0.0% 30d | $6.79 | Launch |
LLMs that fit on the NVIDIA B200
Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 180 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× · 402 GB free |
| Kimi K2.5 Moonshot AI | 1T 32B active | — Not released | — Not released | 714 GB 4× · 5.8 GB free |
| GLM-5.2 Z.ai | 753B | 1,808 GB Too large | 994 GB 8× · 446 GB free | 579 GB 4× · 141 GB free |
| DeepSeek R1 DeepSeek | 671B 37B active | — Not released | 826 GB 8× · 614 GB free or 4× · 4% spare | 481 GB 4× · 239 GB free |
| DeepSeek V4 Flash DeepSeek | 284B | — Not released | — Not released | 192 GB 2× · 168 GB free or 1× · 12% spare |
| Solar Open2 250B Upstage | 250B 15B active | 601 GB 4× · 119 GB free | 330 GB 2× · 30 GB free | 192 GB 2× · 168 GB free or 1× · 12% spare |
| Qwen3 235B-A22B Alibaba | 235B 22B active | 564 GB 4× · 156 GB free | 310 GB 2× · 50 GB free | 181 GB 2× · 179 GB free or 1× · 19% spare |
| Laguna-S 2.1 Poolside | 118B 8B active | 282 GB 2× · 78 GB free | 155 GB 1× · 25 GB free | 90 GB 1× · 90 GB free |
| gpt-oss-120b OpenAI | 117B 5.1B active | — Not released | — Not released | 78 GB 1× · 102 GB free |
| Qwen3.6 35B-A3B Alibaba | 35B 3B active | 86 GB 1× · 94 GB free | 47 GB 1× · 133 GB free | 28 GB 1× · 152 GB free |
| Qwen3 32B Alibaba | 32.8B | 79 GB 1× · 101 GB free | 43 GB 1× · 137 GB free | 25 GB 1× · 155 GB free |
| Gemma 4 31B Google | 31.3B | 75 GB 1× · 105 GB free | 41 GB 1× · 139 GB free | 24 GB 1× · 156 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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