Najemite GPU NVIDIA RTX 2070S na uro
The NVIDIA RTX 2070S is a consumer-segment GPU built on the Turing architecture with 8 GB of VRAM and compute capability 7.5. In ONIE instances, it delivers about 8.8 TFLOPS of standard floating-point performance and about 366 GB/s of memory bandwidth within a 210 W power envelope. You can deploy it with per-hour billing, full SSH access, and ready templates including PyTorch/CUDA, vLLM, Ollama, and ComfyUI.
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 2070S primeren
Compact LLM Inference
Run quantized small language models using Ollama or vLLM templates within the 8 GB VRAM capacity.
PyTorch Prototyping and Debugging
Develop and test deep learning code on a Turing GPU with compute capability 7.5 before scaling to larger hardware.
Lightweight Image Generation
Generate images with ComfyUI using standard diffusion pipelines that fit comfortably inside 8 GB of memory.
GPU Compute Workloads
Execute custom CUDA applications utilizing about 8.8 TFLOPS of single-precision compute and about 366 GB/s of memory bandwidth.
Ali je to pravi GPU za vas?
Choose the RTX 2070S for development, debugging, and lightweight inference tasks that fit within 8 GB of VRAM. Move to a GPU with more memory and higher compute capability for training large models, or pick a lower-power consumer card for basic script testing.
Primerjajte s podobnimi GPU-ji
| GPU | VRAM na GPU | Arhitektura | Računska zmogljivost FP32 | Od / uro | Podrobnosti |
|---|---|---|---|---|---|
| RTX 2070S | 8 GB | Turing | ≈ 8.8 TFLOPS | — | |
| GTX 1080 | 8 GB | Pascal | ≈ 7.8 TFLOPS | — | Podrobnosti |
| Q RTX 4000 | 8 GB | Turing | ≈ 7.7 TFLOPS | — | Podrobnosti |
| RTX 2070 | 8 GB | Turing | ≈ 7.7 TFLOPS | — | Podrobnosti |
Pogosta vprašanja
Is the RTX 2070S suitable for LLM inference or fine-tuning?
With 8 GB of VRAM and about 366 GB/s of memory bandwidth, the RTX 2070S handles inference for small, quantized language models via Ollama or vLLM. Full fine-tuning or running larger models requires a GPU with more VRAM.
Can I use this GPU for image generation with ComfyUI?
Yes, ONIE provides a ready ComfyUI template that runs on the RTX 2070S. Its 8 GB of VRAM supports standard diffusion workflows, though heavier pipelines with multiple control nets will benefit from a higher-memory GPU.
How does the Turing architecture affect software compatibility?
The RTX 2070S supports CUDA compute capability 7.5, which is compatible with standard PyTorch/CUDA templates. However, workloads that require newer architectural features found in later generations should be deployed on a newer GPU architecture.
How do I connect and how does billing work on ONIE?
Instances are billed on a per-hour basis for the time your machine is active. You can connect directly via SSH or launch pre-configured templates for PyTorch/CUDA, vLLM, Ollama, and ComfyUI.
Najamite RTX 2070S po uri
Ustvarite račun, izberite konfiguracijo in se povežite prek SSH. Obračun se ustavi, ko izbrišete GPU.
Najamite RTX 2070S