Najemite GPU NVIDIA RTX 3070 na uro
The NVIDIA RTX 3070 is a consumer-segment Ampere architecture GPU equipped with 8 GB of VRAM and a 220 W TDP. On ONIE, this card provides about 19.8 TFLOPS of single-precision compute and about 386 GB/s of memory bandwidth. With compute capability 8.6, it serves as a practical option for development, prototyping, and lightweight inference tasks.
Specifikacije
VRAM na GPU
Arhitektura
Računska zmogljivost
Računska zmogljivost FP32
Pasovna širina pomnilnika
Omejitev moči
Vrednosti, ki jih za trenutno navedene stroje sporoča ponudnik infrastrukture; dejanska zmogljivost se razlikuje glede na stroj.
Razpoložljive konfiguracije
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Za kaj je RTX 3070 primeren
Lightweight LLM Inference
Run quantized small language models using ready Ollama or vLLM templates within the 8 GB VRAM envelope.
Image Generation Workflows
Generate standard-resolution images and test node workflows using the pre-configured ComfyUI template.
PyTorch Code Prototyping
Develop, debug, and verify machine learning scripts on Ampere hardware with compute capability 8.6 before scaling up.
Batch Data Processing
Utilize about 19.8 TFLOPS of single-precision performance and about 386 GB/s memory bandwidth for GPU-accelerated data pipelines.
Ali je to pravi GPU za vas?
Choose the RTX 3070 for code testing, lightweight inference, and small-scale image generation that fits comfortably inside 8 GB of memory. Select a GPU with more VRAM for fine-tuning or large model weights, or step down to an entry-level card if your workload requires minimal compute.
Primerjajte s podobnimi GPU-ji
| GPU | VRAM na GPU | Arhitektura | Računska zmogljivost FP32 | Od / uro | Podrobnosti |
|---|---|---|---|---|---|
| RTX 3070 | 8 GB | Ampere | ≈ 19.8 TFLOPS | — | |
| RTX 3060 Ti | 8 GB | Ampere | ≈ 15.7 TFLOPS | — | Podrobnosti |
| RTX A4000 | 16 GB | Ampere | ≈ 20.6 TFLOPS | — | Podrobnosti |
| RTX 2080 Ti | 11 GB | Turing | ≈ 14.6 TFLOPS | — | Podrobnosti |
Pogosta vprašanja
Is the RTX 3070 suitable for LLM inference and fine-tuning?
With 8 GB of VRAM, the RTX 3070 can handle inference for small, quantized language models using templates like Ollama or vLLM. For full fine-tuning or running larger models, you should choose a GPU with a larger memory capacity.
How do I connect to my RTX 3070 instance on ONIE?
You can connect directly via SSH access for command-line workflows or launch pre-configured templates including PyTorch/CUDA, ComfyUI, Ollama, and vLLM. Billing is calculated on a per-hour basis while your instance is active.
What CUDA features does the RTX 3070 support?
As an Ampere generation consumer GPU, the RTX 3070 supports compute capability 8.6. This allows it to run modern PyTorch/CUDA workloads that fit within its 8 GB memory limit.
How does the RTX 3070 compare to higher-tier Ampere GPUs?
The RTX 3070 delivers about 19.8 TFLOPS of single-precision compute and about 386 GB/s of memory bandwidth at a 220 W TDP. Stepping up to higher-tier Ampere consumer or workstation GPUs provides increased VRAM capacity and higher memory bandwidth for heavier workloads.
Najamite RTX 3070 po uri
Ustvarite račun, izberite konfiguracijo in se povežite prek SSH. Obračun se ustavi, ko izbrišete GPU.
Najamite RTX 3070