Is the RTX PRO 5000 suitable for LLM inference and fine-tuning?

Yes, its 48 GB of VRAM and about 1057 GB/s of memory bandwidth make it well-suited for serving quantized large models and running LoRA or full fine-tuning on moderate models. You can deploy workloads quickly using pre-configured PyTorch/CUDA, vLLM, or Ollama templates.

How does this GPU compare to smaller consumer cards or larger datacenter GPUs?

As a workstation-class GPU, the RTX PRO 5000 offers significantly more VRAM (48 GB) than standard consumer cards, allowing larger batch sizes and bigger models to fit on a single card. Compared to full datacenter GPUs, it operates at a lower 300 W power envelope and provides a middle ground in memory capacity and compute.

What software support comes with compute capability 12.0?

Compute capability 12.0 corresponds to the NVIDIA Blackwell architecture. You can access this hardware via SSH and use ready templates on Cloudj that include compatible CUDA and PyTorch environments.

How do billing and access work for an RTX PRO 5000 instance?

Instances on Cloudj use per-hour billing, so you only pay for the time the GPU is allocated to you. Once launched with a template like ComfyUI or PyTorch/CUDA, you can connect directly via SSH to manage your files and workflows.

GPU Cloud · NVIDIA · Blackwell

Rent NVIDIA RTX PRO 5000 GPU by the hour

The NVIDIA RTX PRO 5000 is a Blackwell-architecture workstation GPU available for on-demand rental on Cloudj. It pairs 48 GB of VRAM with about 1057 GB/s of memory bandwidth and about 69.6 TFLOPS of single-precision compute at a 300 W TDP. Supporting compute capability 12.0, it handles memory-intensive AI development and professional graphics tasks.

Rent RTX PRO 5000 All GPUs

Specifications

VRAM per GPU

48 GB

Architecture

NVIDIA Blackwell

Compute capability

FP32 compute

≈ 69.6 TFLOPS

Memory bandwidth

≈ 1057 GB/s

Power limit

300 W

Values reported by the infrastructure provider for machines currently listed; real performance varies by machine.

Available configurations

Live stock and prices, updated continuously. The final price is confirmed before you order.

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What the RTX PRO 5000 is good for

Large Language Model Inference

Run local inference and serve medium-to-large open-weight language models within the 48 GB memory allocation using vLLM or Ollama templates.

Model Fine-Tuning and Training

Fine-tune neural networks and diffusion models in PyTorch using Blackwell architecture features and compute capability 12.0.

Generative Media Workflows

Execute complex ComfyUI node graphs and high-resolution image or video generation pipelines without running into consumer VRAM limits.

Workstation Rendering and Simulation

Process heavy geometry, textures, and technical simulations that benefit from 48 GB of memory and about 1057 GB/s bandwidth.

Is it the right GPU for you?

Choose the RTX PRO 5000 when your workloads exceed the memory capacity of consumer cards and require 48 GB of VRAM alongside Blackwell architecture support. Opt for a smaller consumer GPU for lightweight inference, or step up to a larger workstation or datacenter GPU if your models require more than 48 GB of memory or higher multi-GPU interconnect bandwidth.

Compare with similar GPUs

GPU	VRAM per GPU	FP32 compute	Power limit
NVIDIA RTX PRO 5000	48 GB	≈ 69.6 TFLOPS	300 W
NVIDIA RTX 6000Ada	48 GB	≈ 81.4 TFLOPS	300 W	Details →
NVIDIA RTX A6000	48 GB	≈ 36.1 TFLOPS	300 W	Details →
NVIDIA Q RTX 8000	45 GB	≈ 11.9 TFLOPS	250 W	Details →
NVIDIA RTX PRO 4000	24 GB	≈ 44.3 TFLOPS	145 W	Details →

Frequently asked questions

Rent the RTX PRO 5000 by the hour

Create an account, choose a configuration and connect over SSH. Billing stops when you delete the GPU.

Rent RTX PRO 5000 All GPUs