AutoDL — elastic GPU cloud for AI training and inference
On-demand RTX, A100, and H100 rentals—pay-as-you-go instances, community images, and China-friendly billing.
- GPU Cloud
- AI Training
- Container Instances
- Model Fine-Tuning
- Publisher
- AutoDL
- Type
- GPU Cloud & AI Compute Rental
- Pricing
- Freemium
- Reviewed
- 24 August 2026
Quick verdict
Use when
- On-demand GPU containers
- Train / fine-tune / SD / LLM
- Pay-as-you-go billing
- China payment & regions
Skip when
- Hosted inference APIs only
- Local code editor only
- 3D gen UI without GPU VM
AutoDL vs common alternatives
Tap a dimension to focus
Pricing
- AutoDLThis page
- Usage-based GPU
- Optional reserved
- On-demand/reserved pods
- Credit billing
- Marketplace GPU rental
Preview of AutoDL - not the live app. Confirm details on the official site.
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Details below the decision summary—features, workflow, and scope notes.
What is AutoDL?
What it costs
- Free tier
- Yes
- Pricing summary
- AutoDL is primarily usage-based: recharge balance, then pay for GPU time (and storage when disks remain). New accounts may receive promotional credits; students can unlock discounts after verification. Reserved (包年包月) options exist alongside on-demand billing. Confirm live GPU rates and storage fees in AutoDL docs—this page does not list currency amounts; prices change with stock and promotions.
Reviewed on 24 August 2026 · AutoDL — pricing docs
What AutoDL provides
On-demand GPU instances
Rent RTX-class and datacenter GPUs (including A100 / H100 families when in stock) as container instances.
Pay-as-you-go billing
Charge by the second for running GPUs; stop the instance to stop GPU charges while storage fees may continue.
Community images
Start from shared environments for deep learning, diffusion UIs, and common ML stacks instead of bare OS installs.
Storage options
Expandable data disks, file storage, and related products billed separately—confirm current catalog in docs.
APIs for automation
Pro instance APIs support programmatic create/manage flows for power users and teams.
Student and member pricing
Verified students and membership promotions can reduce effective GPU rates—confirm eligibility on-site.
How to use AutoDL
Register and recharge
Create an AutoDL account and top up via the official recharge channels (publisher warns against off-platform payments).
Pick a GPU and image
Choose a region, GPU model, and a community or custom image that matches your stack.
Launch the instance
Start the container, then connect with Jupyter, SSH, or the console tools AutoDL provides.
Train or generate
Run fine-tuning, inference experiments, or creative pipelines on the rented GPU.
Stop when idle
Shut down the GPU to avoid compute charges; download important artifacts and manage disk retention costs.
Who AutoDL is for
Students and researchers
Burst GPU capacity for coursework and papers with student discounts when verified.
Indie ML and generative AI makers
Fine-tune models or run ComfyUI / diffusion stacks on short-lived RTX/A100 instances.
China-based teams
Prefer local payment rails, Chinese support, and domestic regions over overseas GPU marketplaces.
When AutoDL is the right pick
Licensing and platform notes
- Usage-based compute
- Primary model is prepaid balance + per-second GPU billing; reserved plans and storage fees also apply.
- Official recharge only
- Publisher states the official site is the only recharge entrance—avoid third-party top-up scams.
- Console language
- Product UI and docs are primarily Chinese; confirm whether your workflow needs translation help.
- Cloud workloads
- Training data and models reside on AutoDL infrastructure while instances run—review security guidance before uploading sensitive datasets.