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, Cloudj 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 Cloudj?

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.

GPU Cloud · NVIDIA · Turing

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 Cloudj, 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.

Rent RTX 2070 All GPUs

Specifications

VRAM per GPU

8 GB

Architecture

NVIDIA Turing

Compute capability

7.5

FP32 compute

≈ 7.7 TFLOPS

Memory bandwidth

≈ 370 GB/s

Power limit

150 W

Values reported by the infrastructure provider for machines currently listed; real performance varies by machine.

Available configurations

Live stock and prices, updated continuously. The final price is confirmed before you order.

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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

GPU	VRAM per GPU	FP32 compute	Power limit
NVIDIA RTX 2070	8 GB	≈ 7.7 TFLOPS	150 W
NVIDIA GTX 1080	8 GB	≈ 7.8 TFLOPS	130 W	Details →
NVIDIA RTX 2060S	8 GB	≈ 7.5 TFLOPS	175 W	Details →
NVIDIA GTX 1070 Ti	8 GB	≈ 7.4 TFLOPS	180 W	Details →
NVIDIA RTX 2070S	8 GB	≈ 8.8 TFLOPS	210 W	Details →

Frequently asked questions

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 All GPUs