Rent an NVIDIA Q RTX 4000 GPU with 8 GB VRAM
Rent Q RTX 4000 cloud GPU servers by the hour on Cloudj: 8 GB VRAM, NVIDIA Turing and about 7.7 TFLOPS FP32 in an efficient 125 W workstation card. It suits designers, students and developers who need a professional GPU for CAD viewing, light rendering, SD 1.5 images or CUDA testing without paying for a large card. Pick a live configuration, start from the Ubuntu 22.04 + CUDA or PyTorch + JupyterLab template and connect over SSH with your key.
Q RTX 4000 specifications
VRAM per GPU
8 GB
Architecture
NVIDIA Turing
Compute capability
7.5
FP32 compute
≈ 7.7 TFLOPS
Memory bandwidth
≈ 353 GB/s
Power limit
125 W
Values reported by the infrastructure provider for machines currently listed; real performance varies by machine.
Live Q RTX 4000 configurations and prices
From $0.13/h
incl. 32 GB disk · Live price, updated every 15 minutes
What you can run on the Q RTX 4000 cloud GPU
Headless CAD and 3D jobs
Run command-line rendering and model conversion jobs on a professional Turing GPU over SSH.
Stable Diffusion 1.5
8 GB runs SD 1.5 and lightweight SDXL workflows in ComfyUI at modest resolutions.
CUDA development and CI
Compile and test CUDA or PyTorch code on Turing (compute capability 7.5) before you scale to bigger GPUs.
Small model inference
Serve 3B–7B language models at 4-bit through Ollama for demos and internal tools.
Is the Q RTX 4000 the right GPU for your workload?
Choose the Q RTX 4000 for lightweight inference, rendering, and code development that fit inside 8 GB of VRAM and compute capability 7.5. Select a larger GPU with more memory and newer architecture for heavy model training or large-scale inference, or pick a smaller GPU for basic display or minimal compute tasks.
Q RTX 4000 vs similar cloud GPUs
Guides for Q RTX 4000
Q RTX 4000 cloud GPU: frequently asked questions
Rent the Q RTX 4000 cloud GPU by the hour
Pick a live configuration, choose a template and connect over SSH with your key. Billing stops when you destroy the instance.