> For the complete documentation index, see [llms.txt](https://docs.tonic.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tonic.ai/textual/textual-install-administer/configuring-textual/general-instance-and-processing-settings/textual-config-ml-worker-count.md). # Configuring the number of textual-ml workers The `TEXTUAL_ML_WORKERS` environment variable specifies the number of workers to use within the `textual-ml` container. The default value is 1. Having multiple workers allows for parallelization of inferences with NER models. The number of required workers is also affected by the [number of jobs that each worker can run simultaneously](/textual/textual-install-administer/configuring-textual/general-instance-and-processing-settings/config-processing-parallelism.md). When you deploy Textual with Kubernetes on GPUs, parallelization allows the `textual-ml` container to fully utilize the GPU. We recommend 3GB of GPU RAM for each worker. --- # 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.tonic.ai/textual/textual-install-administer/configuring-textual/general-instance-and-processing-settings/textual-config-ml-worker-count.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.