Standard_NC288ds_xl_RTXPRO6000BSE_v6
Azure Virtual Machine: NC288ds_xl_RTXPRO6000BSE_v6 / NC288ds xl_RTXPRO6000BSE_v6 with 288 vCPUs and 1032 GiB of memory. Available in 24 regions starting from $8,030.00 per month.
| Standard_NC288ds_xl_RTXPRO6000BSE_v6 |
| Standard is recommended tier N – GPU enabled C – High-performance computing and machine learning workloads 288 – The number of vCPUs xl – Accelerator Type d – Diskfull (local temp disk is present) s – Premium Storage capable v6 – version |
| 288 |
| x64 |
| 1032 |
| V2 |
| 0 |
| 2 |
| Nvidia RTX PRO 6000 Blackwell Server Edition GPU (96GB) |
| 96 |
| 192 |
| 8 |
| no |
| yes |
| 1023 GiB |
| 2048 GiB |
| 16 |
| yes |
| 450000 |
| 260000 |
| 3815 MiB/Sec |
| 3815 MiB/Sec |
Regional Prices
Region Name | Region ID | Linux Price | Windows Price |
|---|---|---|---|
| Central US | centralus | 11.0000 | 24.2480 |
| East US | eastus | 11.0000 | 24.2480 |
| East US 2 | eastus2 | 11.0000 | 24.2480 |
| hidden-1 | hidden-1 | 11.0000 | 24.2480 |
| hidden-2 | hidden-2 | 11.0000 | 24.2480 |
| hidden-3 | hidden-3 | 13.2000 | 26.4480 |
| hidden-4 | hidden-4 | 13.2000 | 26.4480 |
| hidden-5 | hidden-5 | 13.7600 | 27.0080 |
| hidden-6 | hidden-6 | 13.7600 | 27.0080 |
| hidden-7 | hidden-7 | 13.7600 | 27.0080 |
| hidden-8 | hidden-8 | 13.7600 | 27.0080 |
| hidden-10 | hidden-10 | 14.3000 | 27.5480 |
| hidden-11 | hidden-11 | 14.3000 | 27.5480 |
| hidden-12 | hidden-12 | 14.3000 | 27.5480 |
| hidden-9 | hidden-9 | 14.3000 | 27.5480 |
| hidden-13 | hidden-13 | 14.8500 | 28.0980 |
| hidden-14 | hidden-14 | 15.4000 | 28.6480 |
| hidden-15 | hidden-15 | 15.4000 | 28.6480 |
| hidden-16 | hidden-16 | 15.7300 | 28.9780 |
| hidden-17 | hidden-17 | 15.7300 | 28.9780 |
| hidden-18 | hidden-18 | 15.7300 | 28.9780 |
| hidden-19 | hidden-19 | 15.7400 | 28.9880 |
| hidden-20 | hidden-20 | 15.9600 | 29.2080 |
| hidden-21 | hidden-21 | 15.9600 | 29.2080 |
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Best AI models you can run on this instance
Top open-weight chat models ranked by Intelligence Index that fit in Standard_NC288ds_xl_RTXPRO6000BSE_v6's 192 GB of GPU memory, assuming the model is sharded across all 2 GPUs. VRAM is estimated from the parameter count at the selected quantization, so treat it as guidance rather than a guarantee.
Chat Models
FP16 (full precision)
Model | Creator | Intelligence | Total params | Active params | ~VRAM | |
|---|---|---|---|---|---|---|
| Qwen3.8 27B | #96 | 27B | — | 65 GB | ||
| Qwen3.5 27B | #183 | 27B | — | 65 GB | ||
| JT Flash 35B | Jt | #233 | 35B | — | 84 GB | |
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