NVIDIA RTX PRO 6000 Workstation cloud GPU rental
Rent an NVIDIA RTX PRO 6000 Workstation cloud GPU from $1.83 per hour with 96 GB of GDDR7 at 1.8 TB/s of memory bandwidth. FastInfra bills pods per minute with no commitment, so a one-hour experiment costs $1.83 and you stop paying the moment you delete the pod. Run 1 to 8 GPUs per pod, connect over SSH or JupyterLab, and see the exact price before you deploy.
Prices synced November 3, 2026 · billed per minute
RTX PRO 6000 WK specs and pricing
The NVIDIA RTX PRO 6000 Workstation is a workstation Blackwell GPU with 96 GB of GDDR7 at 1.8 TB/s of memory bandwidth. On FastInfra it is a good fit for 70B+ inference, multi-node training, large-batch fine-tuning. As a rule of thumb, one RTX PRO 6000 WK holds the weights of a model of about 38B parameters at 16-bit precision, or about 150B with 4-bit quantization, after leaving room for the KV cache.
Two on-demand tiers are available. Secure runs in vetted datacenters at $2.80 per GPU-hour; Value uses lower-cost community hosts at $1.83 per GPU-hour for the same GPU model, with more variable capacity.
RTX PRO 6000 WK price per hour by GPU count
Live FastInfra prices per pod-hour. Pods bill per minute, so you pay only for the minutes a pod exists.
| Pod size | Total VRAM | Secure / hr | Value / hr | Month (730 h, lowest) |
|---|---|---|---|---|
| 1× RTX PRO 6000 WK | 96 GB | $2.80 | $1.83 | $1,333 |
| 2× RTX PRO 6000 WK | 192 GB | $5.60 | $3.65 | $2,666 |
| 4× RTX PRO 6000 WK | 384 GB | $11.19 | $7.30 | $5,332 |
| 8× RTX PRO 6000 WK | 768 GB | $22.38 | — | $16,340 |
What an RTX PRO 6000 WK costs over time
One GPU at the lowest on-demand rate ($1.83/hr).
| Duration | On-demand |
|---|---|
| 1 hour | $1.83 |
| 8-hour working day | $14.61 |
| 24 hours | $43.82 |
| 1 week (168 hours) | $307 |
| 1 month (730 hours) | $1,333 |
Which LLMs fit on the RTX PRO 6000 WK?
GPUs needed to hold the model weights with about 20% of VRAM left for the KV cache. Rule of thumb only: long contexts and big batches need more.
| Model size | Examples | FP16 / BF16 | 8-bit | 4-bit |
|---|---|---|---|---|
| 7–8B | Llama 3.1 8B, Qwen3 8B, Mistral 7B | 1 GPU | 1 GPU | 1 GPU |
| 12–14B | Gemma 3 12B, Qwen3 14B, Phi-4 | 1 GPU | 1 GPU | 1 GPU |
| 27–32B | Gemma 3 27B, Qwen3 32B | 1 GPU | 1 GPU | 1 GPU |
| 70B | Llama 3.3 70B | 2 GPUs | 1 GPU | 1 GPU |
| 120B | GPT-OSS 120B | 4 GPUs | 2 GPUs | 1 GPU |
| 235B | Qwen3 235B-A22B | 8 GPUs | 4 GPUs | 2 GPUs |
| 405B | Llama 3.1 405B | Needs more GPUs | 8 GPUs | 4 GPUs |
| 671B | DeepSeek V3 / R1 | Needs more GPUs | Needs more GPUs | 8 GPUs |
Best for
70B+ inference, multi-node training, large-batch fine-tuning.
About the Blackwell architecture
RTX Blackwell cards pair fifth-generation Tensor Cores (with FP4 and FP8 support) with GDDR7 memory. They give much of the inference throughput of datacenter parts at a lower hourly price, and suit LLM serving with quantized weights, image and video generation, and fine-tuning of small and mid-size models. Use a CUDA 12.8+ image for full Blackwell support.
Only need inference?
If you want to call an open model such as Llama, Qwen, DeepSeek or GPT-OSS rather than manage a server, the pay-per-token inference API is often cheaper than keeping a GPU busy around the clock. It is OpenAI-compatible and uses the same account and wallet as your pods. Browse every model with live prices.
RTX PRO 6000 WK vs similar GPUs
Live lowest on-demand price per GPU-hour, and price per GB of VRAM.
| GPU | VRAM | Architecture | From / hr | Per GB-hr |
|---|---|---|---|---|
| RTX PRO 6000 WK (this page) | 96 GB | Blackwell | $1.83 | $0.019 |
| NVIDIA RTX PRO 6000 Max-Q | 96 GB | Blackwell | $1.77 | $0.018 |
| NVIDIA RTX PRO 6000 | 96 GB | Blackwell | $1.83 | $0.019 |
| NVIDIA H100 NVL | 94 GB | Hopper | $2.80 | $0.03 |
| NVIDIA A100 PCIe | 80 GB | Ampere | $1.29 | $0.016 |
| NVIDIA A100 SXM | 80 GB | Ampere | $1.50 | $0.019 |
| NVIDIA H100 PCIe | 80 GB | Hopper | $2.15 | $0.027 |
RTX PRO 6000 WK rental — frequently asked questions
How much does it cost to rent an NVIDIA RTX PRO 6000 Workstation per hour?
$2.80 per GPU-hour on the Secure tier and $1.83 on the Value tier. Billing is per minute while the pod exists, so 24/7 use for a 730-hour month costs about $1,333 per GPU at the lowest on-demand rate. Prices sync from live capacity and are shown before you deploy.
How much VRAM does the NVIDIA RTX PRO 6000 Workstation have?
96 GB of GDDR7 at 1.8 TB/s of memory bandwidth. The chip is rated at up to 600 W. A pod with 8 GPUs gives 768 GB of combined VRAM for tensor- or pipeline-parallel workloads.
What size LLM can I run on an NVIDIA RTX PRO 6000 Workstation?
Roughly 38B parameters at FP16 or BF16, 76B at 8-bit and 150B at 4-bit on a single GPU, reserving about 20% of VRAM for the KV cache and activations. Mixture-of-experts models need memory for all experts, not only the active ones. Long contexts and large batches need more headroom.
Is the NVIDIA RTX PRO 6000 Workstation billed per hour or per minute?
Per minute. The hourly rate is divided by 60 and charged for every minute the pod exists. Attached volumes bill per GB-month and continue while a pod is stopped; delete the pod to stop all charges. There is no minimum term or reservation fee.
How do I rent an NVIDIA RTX PRO 6000 Workstation on FastInfra?
Create an account, add prepaid credit, open the GPU configurator and choose the RTX PRO 6000 WK, the tier, the GPU count, a container template and storage. The configurator shows the hourly and monthly price, then deploys a pod you reach over SSH or JupyterLab within minutes.
Can I rent multiple RTX PRO 6000 WK GPUs in one pod?
Yes, up to 8 per pod (8 on Secure, 4 on Value). The price scales linearly: 8 GPUs cost $22.38 per hour on Secure.
Is there a cheaper GPU than the RTX PRO 6000 WK with the same VRAM?
Yes. The NVIDIA RTX PRO 6000 Max-Q has 96 GB and starts at $1.77 per hour. It differs in architecture (Blackwell), memory bandwidth and multi-GPU interconnect, so compare throughput for your workload before switching.
Deploy an RTX PRO 6000 WK in minutes
Price the exact pod, storage and template in the configurator, add credit, and connect over SSH or JupyterLab. Billed per minute, no commitment.