Principle:InternLM Lmdeploy API Server Deployment
| Knowledge Sources | |
|---|---|
| Domains | LLM_Serving, REST_API |
| Last Updated | 2026-02-07 15:00 GMT |
Overview
A deployment pattern that exposes LLM inference capabilities through an OpenAI-compatible HTTP REST API server with authentication, CORS, and SSL support.
Description
API Server Deployment transforms a local inference pipeline into a production HTTP service. The server implements the OpenAI API specification, providing endpoints for:
- /v1/chat/completions: Chat-based text generation (streaming and non-streaming)
- /v1/completions: Text completion generation
- /v1/models: List available models
- /v1/embeddings: Text embedding generation
The server is built on FastAPI/Uvicorn and includes production features such as API key authentication, CORS configuration, SSL/TLS support, concurrent request limiting, function calling (tool use), and reasoning output parsing. It can be deployed standalone, in Docker containers, or on Kubernetes.
Usage
Use this when you need to serve an LLM over HTTP for integration with existing applications, multi-client access, or production deployment. The OpenAI-compatible API allows drop-in replacement for OpenAI endpoints in existing codebases.
Theoretical Basis
The deployment follows a Client-Server architecture with the LLM engine as the backend:
# Abstract server architecture
server = FastAPI()
engine = AsyncEngine(model, config)
@server.post("/v1/chat/completions")
async def chat(request):
response = await engine.generate(request.messages)
return format_openai_response(response)