# Help designing fire and forget server for large batch inference

**URL:** <https://discuss.ray.io/t/help-designing-fire-and-forget-server-for-large-batch-inference/12953>\
**Category:** Ray Serve\
**Created:** [November 27, 2023, 2:25pm UTC](https://discuss.ray.io/t/help-designing-fire-and-forget-server-for-large-batch-inference/12953 "2023-11-27T14:25:10Z")\
**Posts on this page:** 1\
**Showing post:** 8

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**Author:** ![jonaz](https://avatars.discourse-cdn.com/v4/letter/j/e95f7d/32.png) [@jonaz](https://discuss.ray.io/u/jonaz)\
**Post date:** [November 30, 2023, 10:00am UTC](https://discuss.ray.io/t/help-designing-fire-and-forget-server-for-large-batch-inference/12953/8 "2023-11-30T10:00:44Z")

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Thanks @eoakes for you detailed answers!

> [@eoakes](#):
>
> That is, instead of directly performing the inference in-process, you could make a call to a Ray Serve deployment using the `DeploymentHandle` from the workflow step.

Regarding this, I think I actually tried that but with no success. For some reason, I didn’t manage to make a `deployment_handle.remote()` call from inside a Workflow task/step – probably I’m using the API wrong at some point. I actually made another thread about that here [Workflow calling Deployment.remote()?](https://discuss.ray.io/t/workflow-calling-deployment-remote/12969), maybe you could check it out? I’d really appreciate it 🙂

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