RunPod

Rental GPU cloud for training, fine-tuning and inference

AI Models & PlatformsOverseas★★★★☆ 4.0

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What is RunPod?

RunPod rents GPU capacity by the hour in two shapes: secure cloud instances that behave like a normal virtual machine, and serverless endpoints that scale to zero and bill per second of execution. You get root access, persistent volumes and a choice of community or datacenter GPUs, which makes it a common home for fine-tuning runs, ComfyUI setups and custom inference servers. The differentiator is cost control: community-tier GPUs are cheaper than the large clouds, and the container-based workflow means you bring your own image.

Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.

Key features

On-demand and spot GPU pods with root SSH access
Serverless endpoints with scale to zero and per-second billing
Persistent network volumes that survive pod restarts
Templates for PyTorch, ComfyUI, vLLM and other stacks
Community and secure datacenter GPU pools to choose between
Custom container images pulled from your own registry

Pros & cons

Strengths

Hourly GPU pricing is competitive with larger cloud providers
Root access means you can run any stack you like
Serverless mode removes cost for idle endpoints

Watch out for

Capacity for top-end GPUs sells out at busy times
You still manage drivers, images and scaling yourself
Community cloud hardware varies more in reliability

Best for & use cases

model fine-tuning, gpu experiments, comfyui workflows, custom inference servers

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FAQ

What is the difference between pods and serverless?

Pods are full virtual machines with SSH access, billed by the hour, suited to training and interactive work. Serverless endpoints run your container on demand and bill per second, suited to bursty inference.

Do I need to set up the environment myself?

Mostly yes. Templates cover common stacks like PyTorch and ComfyUI, but for anything custom you bring your own image. That flexibility is why people use it instead of a more managed platform.

Is capacity always available?

No. High-end GPUs are in demand and can be unavailable at short notice, especially on the cheaper community cloud. On-demand pod pricing also changes with supply.