You can’t draw conclusions just by having a GPU. Training requires adapted hardware, VRAM, drivers, frameworks, data and running permissions.
Understand the essentials
Chat applications can be run, which is completely different from training models; some graphical desktops may not allow or be suitable for related calculations.
A practical example
As a hypothetical example: when the browser calls the remote model, the calculation may not be on your desktop GPU.
What to check or try
- First distinguish between invocation, inference and training.
- Review the tool requirements.
- Test sample.
- Check data and fees.
Learners can first understand the data, input and output through small models or supported call examples in the course, and then decide whether they need a training environment. Don't think of buying a more expensive GPU as a learning path. Tasks, tools, resources and costs should be clearly defined layer by layer.
An easy-to-miss detail
Ordinary introductory learning does not need to start with high-cost training, nor should you blindly purchase resources for unknown needs.
Continue reading
- Are AI cloud PCs suitable for everyday users?
- How to confirm that an AI-generated file was actually saved
Back to Cloud PCs: AI and ongoing use
Notes and further reading
Anthropic computer use announcement；Windows 365 for Agents technical information。
This introductory guide was prepared by 51Yunji from public information with AI-assisted drafting. Examples are hypothetical, not hands-on product tests or promises of results. Follow the current product, version and official instructions for specific features and procedures. Compiled on October 26, 2026.