Is the GTX 1080 suitable for LLM inference or fine-tuning?

With 8 GB of VRAM and Pascal architecture, the GTX 1080 can run inference on small, heavily quantized models using templates like Ollama. For larger language models or fine-tuning tasks, a GPU with more VRAM and a newer compute capability is recommended.

What software templates and access methods are available on Cloudj?

You can deploy the GTX 1080 with pre-configured templates including PyTorch/CUDA, ComfyUI, Ollama, and vLLM. Full SSH access is provided so you can customize your environment and manage files directly.

How does the Pascal architecture affect modern deep learning frameworks?

The GTX 1080 supports CUDA compute capability 6.1, which runs standard single-precision operations at about 7.8 TFLOPS. Because it lacks the specialized matrix cores found in newer architectures, frameworks relying on hardware-accelerated lower-precision formats will run slower or require fallback modes.

How does billing work for renting a GTX 1080?

Instances on Cloudj use per-hour billing, so you only pay for the time your GTX 1080 instance is active.

GPU Cloud · NVIDIA · Pascal

Rent NVIDIA GTX 1080 GPU by the hour

The NVIDIA GTX 1080 is a consumer-segment Pascal GPU equipped with 8 GB of VRAM and a 130 W TDP. On Cloudj, it delivers about 7.8 TFLOPS of single-precision compute and about 227 GB/s of memory bandwidth. With compute capability 6.1, it serves entry-level CUDA development, lightweight inference, and basic rendering tasks.

Rent GTX 1080 All GPUs

Specifications

VRAM per GPU

8 GB

Architecture

NVIDIA Pascal

Compute capability

6.1

FP32 compute

≈ 7.8 TFLOPS

Memory bandwidth

≈ 227 GB/s

Power limit

130 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 GTX 1080 is good for

Entry-Level CUDA Development

Write, compile, and debug CUDA code targeting Pascal compute capability 6.1 via SSH access.

Small Model Inference

Run compact neural networks and quantized language models that fit comfortably within 8 GB of VRAM using Ollama or PyTorch templates.

Basic Image Generation

Execute standard image synthesis workflows in ComfyUI where 8 GB of memory and about 227 GB/s bandwidth are sufficient.

Classical Machine Learning

Train and evaluate tabular models or small convolutional networks utilizing about 7.8 TFLOPS of single-precision performance.

Is it the right GPU for you?

Pick the GTX 1080 for prototyping, learning, and lightweight workloads that fit inside 8 GB of VRAM and do not require modern tensor cores. Choose a newer or larger GPU if your workload requires more than 8 GB of memory, native mixed-precision acceleration, or higher memory bandwidth.

Compare with similar GPUs

GPU	VRAM per GPU	FP32 compute	Power limit
NVIDIA GTX 1080	8 GB	≈ 7.8 TFLOPS	130 W
NVIDIA RTX 2070	8 GB	≈ 7.7 TFLOPS	150 W	Details →
NVIDIA RTX 2060S	8 GB	≈ 7.5 TFLOPS	175 W	Details →
NVIDIA GTX 1070 Ti	8 GB	≈ 7.4 TFLOPS	180 W	Details →
NVIDIA RTX 3060 Ti	8 GB	≈ 15.7 TFLOPS	200 W	Details →

Frequently asked questions

Rent the GTX 1080 by the hour

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

Rent GTX 1080 All GPUs