Cloud GPU Planning for Open Image Models

        Do not begin with a provider or a price. Nano Banana 2 needs no rented GPU because it is a Google cloud API. Open-weight models have different requirements, and HunyuanImage 3.0 is a multi-GPU-class deployment in Tencent's official table.
      

Model-first planning

Route	What the official source establishes	Planning action
Nano Banana 2	Google-hosted `gemini-3.1-flash-image`	Use the API; do not rent a GPU for the model
Qwen-Image-Edit-2511	20B BF16; official card gives no universal minimum VRAM	Choose a high-memory CUDA candidate and measure the exact pipeline
HunyuanImage-3.0 base	Official recommendation ≥3 × 80GB	Look for a compatible multi-GPU configuration
HunyuanImage-3.0 Instruct	Official recommendation ≥8 × 80GB	Price the entire configuration, not one A100

For other models, open the exact repository or model card from the quickstart. Community quantizations can reduce memory, but they are separate artifacts and should be tested before you publish a requirement.

What to compare on a cloud marketplace

Total GPUs and VRAM: count every required GPU, not only the memory of one card.
Interconnect and topology: multi-GPU workloads may depend on how the cards communicate.
Software image: match the repository's Python, CUDA, PyTorch, and driver requirements.
Storage: include model weights, caches, containers, and outputs.
Billing state: confirm what continues billing while an instance is stopped.
Current price: inventory and rates change; use the checkout screen as the source of truth.

RunPod setup checklist

1Open current inventory

Open RunPod through this site's referral link, then compare available GPU configurations and current prices. Qualifying usage may support this site at no extra cost to you.

2Match the exact model

Write down the checkpoint, variant, precision, number of inputs, and target resolution. For Hunyuan, check the official Tencent table. For Qwen, begin with the official model card.

3Launch a clean environment

Select a documented PyTorch image with compatible drivers. Install the official project first and generate one baseline output before adding ComfyUI nodes, quantization, or custom kernels.

4Measure before scaling

Record peak VRAM, load time, generation time, output settings, and total bill for one job. That evidence tells you whether to keep the instance, change precision, or choose another model.

5Stop billing deliberately

Save outputs, remove secrets from notebooks, and stop or terminate the resources you no longer need. Check whether storage and public IPs continue billing after compute stops.

Primary sources

Disclosure: the RunPod URL above is a referral link. This page does not claim a fixed hourly price or that one provider is always cheapest; verify current inventory and terms before paying.