NVIDIA RTX PRO 6000 Max-Q cloud GPU rental
Rent an NVIDIA RTX PRO 6000 Max-Q cloud GPU from $1.77 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 $1.77 and you stop paying the moment you delete the pod. Run 1 to 2 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 MaxQ specs and pricing
The NVIDIA RTX PRO 6000 Max-Q 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 MaxQ 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.
The RTX PRO 6000 MaxQ runs on the Value tier, lower-cost community hosts, at $1.77 per GPU-hour.
RTX PRO 6000 MaxQ 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 | Value / hr | Month (730 h, lowest) |
|---|---|---|---|
| 1× RTX PRO 6000 MaxQ | 96 GB | $1.77 | $1,294 |
| 2× RTX PRO 6000 MaxQ | 192 GB | $3.54 | $2,587 |
What an RTX PRO 6000 MaxQ costs over time
One GPU at the lowest on-demand rate ($1.77/hr).
| Duration | On-demand |
|---|---|
| 1 hour | $1.77 |
| 8-hour working day | $14.18 |
| 24 hours | $42.53 |
| 1 week (168 hours) | $298 |
| 1 month (730 hours) | $1,294 |
Which LLMs fit on the RTX PRO 6000 MaxQ?
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 | Needs more GPUs | 2 GPUs | 1 GPU |
| 235B | Qwen3 235B-A22B | Needs more GPUs | Needs more GPUs | 2 GPUs |
| 405B | Llama 3.1 405B | Needs more GPUs | Needs more GPUs | Needs more 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 MaxQ 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 MaxQ (this page) | 96 GB | Blackwell | $1.77 | $0.018 |
| NVIDIA RTX PRO 6000 | 96 GB | Blackwell | $1.83 | $0.019 |
| NVIDIA RTX PRO 6000 Workstation | 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 MaxQ rental — frequently asked questions
How much does it cost to rent an NVIDIA RTX PRO 6000 Max-Q per hour?
$1.77 per GPU-hour on demand. Billing is per minute while the pod exists, so 24/7 use for a 730-hour month costs about $1,294 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 Max-Q have?
96 GB of GDDR7 at 1.8 TB/s of memory bandwidth. The chip is rated at up to 300 W. A pod with 2 GPUs gives 192 GB of combined VRAM for tensor- or pipeline-parallel workloads.
What size LLM can I run on an NVIDIA RTX PRO 6000 Max-Q?
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 Max-Q 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 Max-Q on FastInfra?
Create an account, add prepaid credit, open the GPU configurator and choose the RTX PRO 6000 MaxQ, 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 MaxQ GPUs in one pod?
Yes, up to 2 per pod. The price scales linearly: 2 GPUs cost $3.54 per hour on Value.
Deploy an RTX PRO 6000 MaxQ 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.