What is the GTX 1080 still good for?

FP32 compute, learning, legacy code and small quantized models. With 8 GB and 227 GB/s it is not suited to SDXL, Flux or LLMs above about 7B parameters.

GTX 1080 vs GTX 1080 Ti?

The GTX 1080 Ti is listed with 11 GB, about 11 TFLOPS and 339 GB/s, against 8 GB, 7.8 TFLOPS and 227 GB/s here. The extra 3 GB lets the Ti run 13B models at 4-bit.

Does the GTX 1080 support current frameworks?

It has compute capability 6.1, which some recent CUDA builds have dropped. Check the CUDA and framework versions in your template or Docker image before you deploy.

How do I connect to a GTX 1080 server on Cloudj?

Over SSH with your key; password login is disabled. The instance page shows the host, port and full SSH command. GPU instances have no browser terminal or graphical desktop.

GPU Cloud · NVIDIA · Pascal

Rent an NVIDIA GTX 1080 GPU with 8 GB VRAM

Rent GTX 1080 cloud GPU servers by the hour on Cloudj: 8 GB VRAM on NVIDIA Pascal with about 7.8 TFLOPS FP32 at a modest 130 W power limit. It is a simple, no-surprises GPU for coursework, legacy CUDA projects and small inference jobs where you just need a working GPU over SSH. Check that your CUDA and framework versions still support this GPU, pick a live configuration and connect over SSH with your key.

Rent GTX 1080 All GPUs

GTX 1080 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.

Live GTX 1080 configurations and prices

Out of stock right now

Browse available GPUs

What you can run on the GTX 1080 cloud GPU

Legacy CUDA projects

Rebuild and run older CUDA or OpenCL code that was written and tuned for Pascal GPUs.

University coursework

Run assignments in PyTorch or TensorFlow on a dedicated GPU instead of a shared lab machine.

Lightweight LLM tests

Try 3B–7B models at 4-bit with Ollama to check prompts and integrations.

Image processing batches

Resize, filter or run classic vision models over image sets with GPU acceleration.

Is the GTX 1080 the right GPU for your workload?

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.

GTX 1080 vs similar cloud 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 →

Guides for GTX 1080

GPU cloud Asia guide: Japan, Singapore, Korea and nearbyA practical GPU cloud Asia guide: which country codes to search for East and Southeast Asian users, a fallback order, and how to pair a GPU with a VPS.GPU cloud by location: how to pick a country for your GPUHow GPU cloud by location works on Cloudj: filter a live list of machines by country code, decide between near users, lowest price or data rules.GPU cloud Europe: choosing a country for your AI workloadsA practical guide to GPU cloud Europe searches: filter by several country codes at once, weigh Western, Nordic and Eastern hosts, and plan where data lives.GPU cloud North America: renting GPUs in the US and CanadaA GPU cloud North America guide: how to search US and CA hosts, read the results, pick a VPS front end and decide when a US GPU suits users elsewhere.

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GTX 1080 cloud GPU: frequently asked questions

Rent the GTX 1080 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.

Rent GTX 1080 All GPUs