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 Cloudj?

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.

GPU Cloud · NVIDIA · Ampere

Rent NVIDIA RTX 3070 GPU by the hour

The NVIDIA RTX 3070 is a consumer-segment Ampere architecture GPU equipped with 8 GB of VRAM and a 220 W TDP. On Cloudj, 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.

Rent RTX 3070 All GPUs

Specifications

VRAM per GPU

8 GB

Architecture

NVIDIA Ampere

Compute capability

8.6

FP32 compute

≈ 19.8 TFLOPS

Memory bandwidth

≈ 386 GB/s

Power limit

220 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 3070 is good for

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.

Is it the right GPU for you?

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.

Compare with similar GPUs

GPU	VRAM per GPU	FP32 compute	Power limit
NVIDIA RTX 3070	8 GB	≈ 19.8 TFLOPS	220 W
NVIDIA RTX 3060 Ti	8 GB	≈ 15.7 TFLOPS	200 W	Details →
NVIDIA RTX 2070S	8 GB	≈ 8.8 TFLOPS	210 W	Details →
NVIDIA GTX 1080	8 GB	≈ 7.8 TFLOPS	130 W	Details →
NVIDIA RTX 2070	8 GB	≈ 7.7 TFLOPS	150 W	Details →

Frequently asked questions

Rent the RTX 3070 by the hour

Create an account, choose a configuration and connect over SSH. Billing stops when you delete the GPU.

Rent RTX 3070 All GPUs