GPU Cloud · NVIDIA · Pascal

Nuomokite NVIDIA GTX 1080 GPU per valandą

The NVIDIA GTX 1080 is a consumer-segment Pascal GPU equipped with 8 GB of VRAM and a 130 W TDP. On ONIE, 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.

Specifikacijos

VRAM kiekvienam GPU

8 GB

Architektūra

NVIDIA Pascal

Compute capability

6.1

FP32 skaičiavimas

≈ 7.8 TFLOPS

Atminties pralaidumas

≈ 227 GB/s

Galios riba

130 W

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Turimos konfigūracijos

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Kam tinka GTX 1080

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.

Ar tai tinkamas GPU jums?

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.

Palyginti su panašiais GPU

GPUVRAM kiekvienam GPUArchitektūraFP32 skaičiavimasNuo / valandąIšsamiau
GTX 10808 GBPascal≈ 7.8 TFLOPS—
Q RTX 40008 GBTuring≈ 7.7 TFLOPS—Išsamiau
RTX 20708 GBTuring≈ 7.7 TFLOPS—Išsamiau
RTX 2060S8 GBTuring≈ 7.5 TFLOPS—Išsamiau

Dažniausiai užduodami klausimai

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 ONIE?

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

Nuomokite GTX 1080 valandomis

Sukurkite paskyrą, pasirinkite konfigūraciją ir prisijunkite per SSH. Mokėjimas nutraukiamas, kai ištrinate GPU.

Nuomoti GTX 1080

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