Is the RTX 4070S suitable for LLM inference and image generation?

Yes, the 12 GB of VRAM and about 411 GB/s of memory bandwidth handle compact or quantized language models and standard diffusion workflows well. You can deploy these workloads directly on Cloudj using ready templates for Ollama, vLLM, and ComfyUI.

How does this GPU compare to larger or smaller options?

As a 12 GB consumer-segment card, the RTX 4070S offers more memory headroom and compute than entry-level GPUs while maintaining a modest 220 W TDP. However, if you are training large networks or running unquantized models that exceed 12 GB, you should select a higher-tier consumer or workstation GPU with a larger memory capacity.

What software templates and access methods are included?

Every instance on Cloudj includes direct SSH access so you can manage your environment from your terminal. You can also launch instances with pre-configured templates for PyTorch/CUDA, vLLM, Ollama, and ComfyUI.

How does billing work for the RTX 4070S on Cloudj?

Instances are billed on a per-hour basis, allowing you to pay only for the time your GPU is active. You can spin up an environment for a short prototyping session and terminate it when your job finishes.

GPU Cloud · NVIDIA · Ada Lovelace

Rent NVIDIA RTX 4070S GPU by the hour

The NVIDIA RTX 4070S is a consumer-segment GPU built on the Ada Lovelace architecture with CUDA compute capability 8.9. Available to rent on Cloudj, it equips your environment with 12 GB of VRAM, about 35.8 TFLOPS of standard floating-point compute, and a memory bandwidth of about 411 GB/s. Operating at a 220 W TDP, this card handles mid-sized generative AI and development tasks with per-hour billing and full SSH access.

Rent RTX 4070S All GPUs

Specifications

VRAM per GPU

12 GB

Architecture

NVIDIA Ada Lovelace

Compute capability

8.9

FP32 compute

≈ 35.8 TFLOPS

Memory bandwidth

≈ 411 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 4070S is good for

Quantized LLM Inference

Run compact or quantized language models within the 12 GB VRAM allocation using pre-configured Ollama or vLLM templates.

Diffusion Image Generation

Generate and iterate on visual assets with ComfyUI, utilizing the Ada Lovelace architecture and about 35.8 TFLOPS of compute.

PyTorch Model Prototyping

Develop, test, and debug deep learning code using the ready PyTorch/CUDA template on hardware supporting compute capability 8.9.

Fine-Tuning Small Models

Execute parameter-efficient fine-tuning jobs and lightweight training runs that fit comfortably inside a 12 GB memory ceiling.

Is it the right GPU for you?

Choose the RTX 4070S when your workloads require modern Ada Lovelace features and compute capability 8.9 within a 12 GB memory envelope. Opt for a smaller GPU for basic scripts and very light inference, or step up to a larger workstation or datacenter GPU if your models exceed 12 GB of VRAM or require higher than about 411 GB/s of memory bandwidth.

Compare with similar GPUs

GPU	VRAM per GPU	FP32 compute	Power limit
NVIDIA RTX 4070S	12 GB	≈ 35.8 TFLOPS	220 W
NVIDIA RTX 3080 Ti	12 GB	≈ 35.1 TFLOPS	350 W	Details →
NVIDIA RTX 4070	12 GB	≈ 29.3 TFLOPS	200 W	Details →
NVIDIA GTX 1080 Ti	11 GB	≈ 11 TFLOPS	250 W	Details →
NVIDIA GTX 1080	8 GB	≈ 7.8 TFLOPS	130 W	Details →

Frequently asked questions

Rent the RTX 4070S by the hour

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

Rent RTX 4070S All GPUs