Is the RTX 3080 suitable for LLM inference and image generation?

Yes, its 10 GB of VRAM and about 656 GB/s of memory bandwidth handle standard image generation in ComfyUI and quantized language models via Ollama or vLLM. Larger unquantized models will require a GPU with a higher memory capacity.

What software templates are available for the RTX 3080 on Cloudj?

You can launch instances with ready templates for PyTorch/CUDA, vLLM, Ollama, and ComfyUI. The card supports compute capability 8.6 for broad compatibility with modern machine learning frameworks.

When should I step up from the RTX 3080 to a larger GPU?

The RTX 3080 provides strong consumer-class compute at about 29.5 TFLOPS within a 380 W power envelope, but it is limited to 10 GB of VRAM. If your training batches or model weights cause out-of-memory errors, a workstation or datacenter GPU with a larger memory buffer is the appropriate choice.

How do billing and access work on Cloudj?

Instances are billed on a per-hour basis while active. You receive full SSH access to manage your environment alongside any web interfaces provided by your chosen template.

GPU Cloud · NVIDIA · Ampere

Rent NVIDIA RTX 3080 GPU by the hour

The NVIDIA RTX 3080 is an Ampere-architecture consumer GPU equipped with 10 GB of VRAM. It delivers about 29.5 TFLOPS of standard floating-point compute and a memory bandwidth of about 656 GB/s. On Cloudj, you can deploy this GPU with per-hour billing, SSH access, and ready templates.

Rent RTX 3080 All GPUs

Specifications

VRAM per GPU

10 GB

Architecture

NVIDIA Ampere

Compute capability

8.6

FP32 compute

≈ 29.5 TFLOPS

Memory bandwidth

≈ 656 GB/s

Power limit

380 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 3080 is good for

Compact LLM Inference

Run quantized language models that fit within 10 GB of VRAM using pre-configured Ollama or vLLM templates.

Image Generation Workflows

Execute diffusion pipelines in ComfyUI leveraging about 656 GB/s of memory bandwidth for steady image output.

PyTorch Model Prototyping

Develop and test deep learning code on an Ampere card with compute capability 8.6 before scaling up.

CUDA Compute Tasks

Accelerate custom workloads that utilize about 29.5 TFLOPS of standard floating-point performance.

Is it the right GPU for you?

Choose the RTX 3080 for prototyping, image generation, and small-scale inference where 10 GB of VRAM and strong Ampere compute are sufficient. Opt for a GPU with more memory if your models or batch sizes exceed 10 GB, or select a lower-spec consumer card for lightweight scripts that do not require about 29.5 TFLOPS.

Compare with similar GPUs

GPU	VRAM per GPU	FP32 compute	Power limit
NVIDIA RTX 3080	10 GB	≈ 29.5 TFLOPS	380 W
NVIDIA GTX 1080 Ti	11 GB	≈ 11 TFLOPS	250 W	Details →
NVIDIA RTX 4070	12 GB	≈ 29.3 TFLOPS	200 W	Details →
NVIDIA RTX 3080 Ti	12 GB	≈ 35.1 TFLOPS	350 W	Details →
NVIDIA RTX 4070S	12 GB	≈ 35.8 TFLOPS	220 W	Details →

Frequently asked questions

Rent the RTX 3080 by the hour

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

Rent RTX 3080 All GPUs