Rent NVIDIA RTX 2070 GPU by the hour
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.
Specifications
VRAM per GPU
Architecture
Compute capability
FP32 compute
Memory bandwidth
Power limit
Values reported by the infrastructure provider for machines currently listed; real performance varies by machine.
Available configurations
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What the RTX 2070 is good for
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.
Is it the right GPU for you?
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.
Compare with similar GPUs
Frequently asked questions
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.
Rent the RTX 2070 by the hour
Create an account, choose a configuration and connect over SSH. Billing stops when you delete the GPU.
Rent RTX 2070