---
title: "Overview"
description: ""
position: 1
---
Model servers are stateless LLM inference endpoints. They receive a conversation and return the model's next output (text, tool calls, or code) with no memory or orchestration logic. During training, you will typically have at least one active Model server: the "policy" model being trained.

Any OpenAI-compatible inference backend can serve as a Model server. NeMo Gym provides middleware to bridge format differences (e.g., converting between Chat Completions and Responses API schemas).

Model servers implement `ResponsesAPIModel` and expose two endpoints:

- **`/v1/responses`** — [OpenAI Responses API](https://developers.openai.com/api/reference/resources/responses/methods/create)
  - This is the default input/output schema for all NeMo Gym rollouts.
- **`/v1/chat/completions`** — [OpenAI Chat Completions API](https://developers.openai.com/api/reference/resources/chat/subresources/completions/methods/create)

## Backend Guides

Guides for OpenAI and Azure OpenAI Responses API models and more are coming soon!

<Cards>

<Card title="vLLM" href="/v0.2/model-server/vllm">
Self-hosted inference with vLLM for maximum control.

<Badge minimal outlined>self-hosted</Badge> <Badge minimal outlined>open-source</Badge>
</Card>

</Cards>

## Server Configuration
<Note>
[Model Server Fields](/v0.2/reference/configuration#model-server-fields) for server configuration syntax and fields.

</Note>
