> For the complete documentation index, see [llms.txt](https://docs.zerve.ai/guide/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zerve.ai/guide/notebook-view/blocks/compute-settings/compute-types.md). # Compute Types ## Lambda vs Fargate Lambda uses AWS lambda to execute code. This is suitable for tasks up to 15 minutes of execution time and has 10GB of memory Fargate mode lets the user select custom compute CPUs and suitable for long running tasks. This can support CPUs upto 120GB memory. ## **GPU** Zerve canvas also allow users to select from a range of GPUs with varying performance, memory capacities, and features for heavy compute tasks like training and fine-tuning in deep learning, LLMs etc. ## Kubernetes Kubernetes mode runs the code on a self-hosted Kubernetes cluster. To run code on a Kubernetes cluster, enable Kubernetes on the Self-Hosting Settings page. --- # 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://docs.zerve.ai/guide/notebook-view/blocks/compute-settings/compute-types.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.