Principle:Intel Ipex llm LLM Serving With FastAPI
| Knowledge Sources | |
|---|---|
| Domains | Serving, REST_API, Deployment |
| Last Updated | 2026-02-09 04:00 GMT |
Overview
Serving pattern that exposes LLM inference as REST API endpoints using FastAPI with support for streaming, batching, and distributed backends.
Description
LLM serving via FastAPI provides a standardized HTTP interface for model inference. The pattern supports multiple backend configurations: single-GPU lightweight serving (FastApp + ModelWorker), multi-GPU tensor parallelism (DeepSpeed AutoTP), and multi-GPU pipeline parallelism (PPModelWorker). All variants provide streaming and non-streaming endpoints compatible with the OpenAI API format. The async request processing enables batching of concurrent requests for improved throughput.
Usage
Use this principle when deploying LLM models as HTTP services for application integration. Choose the appropriate backend based on model size: lightweight for models that fit on one GPU, DeepSpeed AutoTP for tensor parallelism, or PPModelWorker for pipeline parallelism.
Theoretical Basis
Pseudo-code Logic:
# Abstract serving pattern
model = load_and_optimize(model_path, quantization)
worker = create_worker(model, backend_type)
app = FastAPI()
@app.post("/generate")
async def generate(request):
return await worker.generate(request.prompt)
@app.post("/generate_stream")
async def stream(request):
return StreamingResponse(worker.stream_generate(request.prompt))