> For the complete documentation index, see [llms.txt](https://fall2019.fullstackdeeplearning.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://fall2019.fullstackdeeplearning.com/course-content/infrastructure-and-tooling/hardware.md). # Computing and GPUs {% embed url="" %} Computing and GPUS - Infrastructure and Tooling {% endembed %} ## Summary * If you go with the GPU round, there are a lot of **NVIDIA** cards to choose from (Kepler, Maxwell, Pascal, Volta, Turing). * If you go with a cloud provider, **Amazon Web Services** and **Google Cloud Platform** are the heavyweights, while startups such as **Paperspace** and **Lambda Labs** are also viable options. * If you work solo or in a startup, you should build or buy a 4x recent-architecture PC for model development. For model training, if you run many experiments, you can either buy shared server machines or use cloud instances. * If you work in a large company, you are more likely to rely on cloud instances for both model development and model training, as they provide proper provisioning and infrastructure to handle failures. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://fall2019.fullstackdeeplearning.com/course-content/infrastructure-and-tooling/hardware.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.