# 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: 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: ``` GET https://fall2019.fullstackdeeplearning.com/course-content/infrastructure-and-tooling/hardware.md?ask= ``` The question should be specific, self-contained, and written in natural language. 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.