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RTX 4090 on RunPod: Pricing, Specs & How to Rent

Deploybase · June 4, 2025 · GPU Pricing

Contents

RTX 4090 GPU Specifications

The RTX 4090: 16,384 CUDA cores, 24GB GDDR6X, 82.6 TFLOPS (FP32). 1,008 GB/s bandwidth. 450W peak power.

Good for image generation (Stable Diffusion), small LLM inference, computer vision. Fits Mistral 7B, Llama 2 13B, and 20B models with quantization. Hits memory limits on bigger models or large batch sizes.

RunPod RTX 4090 Pricing

RunPod charges $0.34/hr. Cheap for cloud GPU compute. Good for dev or tight budgets.

Billing by the minute, 1-minute minimum. 8 hours = $2.72. Encourages experimentation.

Spot instances 30-50% cheaper, prices fluctuate. Good for batch jobs that tolerate interruption.

Persistent storage separate from compute. Models don't reupload. Network egress costs extra.

How to Rent RTX 4090 on RunPod

  1. Create account at runpod.io. Verify email, add billing.

  2. Click "Create Pod" → search "RTX 4090" → pick region.

  3. Choose config. For image gen or small models, basic template works. 20GB storage for model weights.

  4. Pick pre-built image (Stable Diffusion WebUI, ComfyUI, CUDA) or custom Docker image.

  5. Provision in 30 seconds to 2 minutes. Get IP and port.

  6. SSH or hit web interface directly. Deploy inference server or dev environment.

RTX 4090 for Different Workloads

Image Generation

40-60 iterations/minute depending on resolution. Batch or interactive use.

LLM Inference

Handles 13B models without quantization. Larger models need 8-bit or 4-bit. Latency: 50-200ms per query.

Training

Fine-tuning works. Full training of large models needs multiple GPUs.

Dev & Testing

Cheap way to validate before scaling to H100 or A100. Find bugs cheaply.

FAQ

Is 24GB memory enough for model inference on RTX 4090? 24GB accommodates models up to 13B parameters without quantization. Larger models need 4-bit or 8-bit quantization to fit. With quantization, models up to 30B parameters run successfully.

Can I train custom models on RTX 4090? Yes, training smaller models or fine-tuning existing models works well. Batch sizes remain small due to memory constraints. For large-scale training, multiple GPUs or higher-memory GPUs become necessary.

How fast is image generation on RTX 4090 compared to A100? RTX 4090 and A100 perform similarly for image generation at similar resolutions. The A100's value proposition lies in higher throughput for large batch sizes, not individual image speed.

What is the typical bandwidth utilization for RTX 4090? Most inference workloads achieve 60 to 80 percent of the memory bandwidth potential. Image generation typically maxes out memory bandwidth due to the computational patterns involved.

Can multiple RTX 4090s work together on RunPod? RunPod supports multi-GPU pods. Two or four RTX 4090s can be rented together and configured for distributed inference or training.

RTX 4090 Detailed Specifications and Benchmarks provide in-depth technical analysis.

GPU Pricing Comparison Guide shows how RTX 4090 rates compare across providers.

L40S Specifications describe a professional alternative for inference workloads.

Sources