Is the Q RTX 4000 suitable for LLM inference and fine-tuning?

With 8 GB of VRAM and about 7.7 TFLOPS of single-precision performance, it is suitable for inference with compact models using Ollama or vLLM. For larger models or full fine-tuning workloads, you should choose a GPU with more VRAM and higher memory bandwidth than about 353 GB/s.

Can I use this GPU for ComfyUI and image generation?

Yes, Cloudj offers a ready ComfyUI template that runs on this Turing workstation GPU. It handles standard generation pipelines that fit into 8 GB of memory, though larger pipelines will require a GPU with a bigger memory allocation.

How do I connect to the instance and what software is pre-installed?

You can connect directly to your running instance via SSH access. Cloudj provides pre-configured templates including PyTorch/CUDA, vLLM, Ollama, and ComfyUI so you can start working immediately.

How does billing work for the Q RTX 4000 on Cloudj?

Instances are billed on a per-hour basis while your workstation GPU is active. This allows you to run development or rendering sessions and terminate the instance when finished.

GPU Cloud · NVIDIA · Turing

Rent NVIDIA Q RTX 4000 GPU by the hour

The NVIDIA Q RTX 4000 is a Turing-based workstation GPU available to rent on Cloudj for entry-level AI and graphics workloads. It provides 8 GB of VRAM, about 7.7 TFLOPS of single-precision compute, and a memory bandwidth of about 353 GB/s within a 125 W power profile. Supporting CUDA compute capability 7.5, this instance can be deployed with ready templates for PyTorch/CUDA, vLLM, Ollama, and ComfyUI.

Rent Q RTX 4000 All GPUs

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.

Available configurations

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

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What the Q RTX 4000 is good for

Code Prototyping and Debugging

Test and validate PyTorch/CUDA pipelines on an 8 GB workstation GPU before scaling up to larger hardware.

Compact Model Inference

Run small language models through Ollama or vLLM that fit comfortably within 8 GB of VRAM.

Basic Image Generation

Execute standard image generation workflows in ComfyUI that do not exceed the 8 GB memory limit.

Workstation Graphics and Rendering

Utilize the Turing architecture and about 353 GB/s of memory bandwidth for remote rendering and visualization tasks.

Is it the right GPU for you?

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.

Compare with similar GPUs

GPU	VRAM per GPU	FP32 compute	Power limit
NVIDIA Q RTX 4000	8 GB	≈ 7.7 TFLOPS	125 W
NVIDIA RTX A2000	6 GB	≈ 11.2 TFLOPS	70 W	Details →
NVIDIA RTX A4000	16 GB	≈ 20.6 TFLOPS	140 W	Details →
NVIDIA RTX 4500Ada	24 GB	≈ 38.2 TFLOPS	210 W	Details →
NVIDIA RTX PRO 4000	24 GB	≈ 44.3 TFLOPS	145 W	Details →

Frequently asked questions

Rent the Q RTX 4000 by the hour

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

Rent Q RTX 4000 All GPUs