Изнајмите NVIDIA RTX 2070 GPU по сату
The NVIDIA RTX 2070 is a consumer-segment GPU built on the Turing architecture with compute capability 7.5 and 8 GB of VRAM. On ONIE, instances report about 7.7 TFLOPS of single-precision compute and about 370 GB/s of memory bandwidth within a 150 W power envelope. You can deploy this card with per-hour billing, full SSH access, and ready templates for PyTorch/CUDA, vLLM, Ollama, and ComfyUI.
Спецификације
VRAM по GPU-у
Архитектура
Compute capability
FP32 рачунање
Меморијска пропусност
Ограничење снаге
Вредности које пријављује добављач инфраструктуре за тренутно наведене машине; стварне перформансе се разликују од машине до машине.
Доступне конфигурације
Актуелне залихе и цене, ажурирају се непрекидно. Коначна цена се потврђује пре наручивања.
Учитавање цена у живо…
За шта је RTX 2070 добар
Compact Language Model Inference
Run quantized or small language models that fit inside the 8 GB VRAM capacity using pre-configured Ollama or vLLM templates.
Basic Image Generation
Generate standard-resolution images and test diffusion workflows in ComfyUI within the limits of an 8 GB memory buffer.
PyTorch Code Prototyping
Develop, debug, and test neural network training scripts in a PyTorch/CUDA environment before scaling to larger hardware.
Entry-Level CUDA Development
Compile and test custom kernels targeting Turing architecture and compute capability 7.5 over direct SSH access.
Да ли је то прави GPU за вас?
Choose the RTX 2070 for lightweight development, code debugging, and small-scale inference tasks that fit comfortably within 8 GB of VRAM. Opt for a larger GPU if your models require more memory or higher throughput than about 7.7 TFLOPS, or select a smaller GPU only for minimal display or basic compute testing.
Упоредите са сличним GPU-овима
Често постављана питања
Is the RTX 2070 suitable for LLM inference and fine-tuning?
With 8 GB of VRAM and about 370 GB/s of memory bandwidth, the RTX 2070 can handle inference for small, quantized language models via Ollama or vLLM. For larger models or full fine-tuning workloads, a GPU with more VRAM and newer architecture is recommended.
Can I use this GPU for image generation with ComfyUI?
Yes, ONIE provides a ComfyUI template that runs on the RTX 2070. Its 8 GB of VRAM is sufficient for standard generation pipelines, though complex workflows with multiple control nets or upscale passes will benefit from a card with a larger memory capacity.
How does the RTX 2070 compare to other consumer GPUs?
As a Turing-generation consumer card with compute capability 7.5 and a 150 W power draw, it sits below newer architectures in raw compute and memory capacity. Stepping up to a higher-tier model provides more VRAM and higher TFLOPS for heavier training, while stepping down reduces memory headroom further.
How do billing and instance access work on ONIE?
Instances are billed on a per-hour basis while active. You can connect directly via SSH or launch immediately into pre-configured environments including PyTorch/CUDA, vLLM, Ollama, and ComfyUI.
Изнајмите RTX 2070 по сату
Креирајте налог, изаберите конфигурацију и повежите се преко SSH-а. Наплата престаје када обришете GPU.
Изнајмите RTX 2070