The NVIDIA RTX PRO 6000 is an enterprise-class GPU built on the Blackwell architecture, featuring 96 GB of memory. Currently available from 9 providers starting at $0.50/GPU/hour, with a market median of $1.89/GPU/hour across 53 configurations.
Hardware specifications
Same across all providers
Median price across all providers
10 offerings from 9 providers
Sorted by price ascending. Compare configurations side-by-side.
| Provider | Count | vCPU | RAM | Region | Per GPU hour | Total/hr | Action |
|---|---|---|---|---|---|---|---|
| ×1 | 16 | 140 GB | $2.090 | $2.09 | Launch | ||
| ×4 | 56 | 409 GB | From$0.890 -5.4% 30d | From$3.56 | Launch | ||
| ×1 | — | 146 GB | $1.680 0.0% 30d | $1.68 | Launch | ||
| ×1 | 28 | 225 GB | $1.850 | From$1.85 | |||
| ×1 | 30 | 90 GB | -- | $1.890 | From$1.89 | Launch | |
Up to$500credit | ×1 | 16 | 140 GB | From$2.090 -9.6% 30d | From$2.09 | Launch | |
| ×1 | 16 | 144 GB | $2.140 0.0% 30d | From$2.14 | Launch | ||
| ×1 | 16 | 192 GB | From$2.300 0.0% 30d | From$2.30 | Launch | ||
Sesterce CloudReferral link | ×1 | 16 | 192 GB | From$2.409 0.0% 30d | From$2.41 | Launch | |
| ×1 | 16 | 192 GB | $2.410 +12.1% 30d | From$2.41 | Launch | ||
| ×8 | 128 | 1.5 TB | From$6.000 0.0% 30d | From$48.00 | Launch |
LLMs that fit on the NVIDIA RTX PRO 6000
Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 96 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 8× · 54 GB free |
| GLM-5.2 Z.ai | 753B | 1,808 GB Too large | 994 GB Too large | 579 GB 8× · 189 GB free |
| DeepSeek R1 DeepSeek | 671B 37B active | — Not released | 826 GB Tight on 8× · 11% spare | 481 GB 8× · 287 GB free |
| DeepSeek V4 Flash DeepSeek | 284B | — Not released | — Not released | 192 GB Barely fits on 2× |
| Solar Open2 250B Upstage | 250B 15B active | 601 GB 8× · 167 GB free | 330 GB 4× · 54 GB free | 192 GB 4× · 192 GB free or 2× · 19% spare |
| Qwen3 235B-A22B Alibaba | 235B 22B active | 564 GB 8× · 204 GB free | 310 GB 4× · 74 GB free | 181 GB 2× · 11 GB free |
| Laguna-S 2.1 Poolside | 118B 8B active | 282 GB 4× · 102 GB free | 155 GB 2× · 37 GB free | 90 GB 1× · 5.7 GB free |
| gpt-oss-120b OpenAI | 117B 5.1B active | — Not released | — Not released | 78 GB 1× · 18 GB free |
| Qwen3.6 35B-A3B Alibaba | 35B 3B active | 86 GB 1× · 9.7 GB free | 47 GB 1× · 49 GB free | 28 GB 1× · 68 GB free |
| Qwen3 32B Alibaba | 32.8B | 79 GB 1× · 17 GB free | 43 GB 1× · 53 GB free | 25 GB 1× · 71 GB free |
| Gemma 4 31B Google | 31.3B | 75 GB 1× · 21 GB free | 41 GB 1× · 55 GB free | 24 GB 1× · 72 GB free |
| GLM-4.7 Flash Z.ai | 31.2B | 75 GB 1× · 21 GB free | 41 GB 1× · 55 GB free | 24 GB 1× · 72 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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