LitServe — Fast AI Model Serving Engine

What it is

LitServe is a high-performance AI model serving engine built on top of FastAPI by Lightning AI. It adds batching, streaming, GPU management, and autoscaling to make deploying AI models simple and fast. You define a LitAPI class with setup and predict methods, and LitServe handles the rest.

It is designed for ML engineers who need to deploy models to production without building custom serving infrastructure from scratch.

How it saves time or tokens

The token estimate for this workflow is 3,800 tokens. LitServe claims 2x throughput over plain FastAPI by batching requests automatically and managing GPU memory. The multi-model endpoint feature lets you serve multiple models on one server, reducing infrastructure costs.

How to use

1. Install: pip install litserve
2. Define a LitAPI class with setup() and predict() methods
3. Create a LitServer and call server.run()

Example

import litserve as ls

class MyAPI(ls.LitAPI):
    def setup(self, device):
        # Load model to the given device (cpu/gpu)
        self.model = load_model(device)

    def decode_request(self, request):
        return request['input']

    def predict(self, x):
        return self.model(x)

    def encode_response(self, output):
        return {'output': output}

server = ls.LitServer(MyAPI(), accelerator='gpu', devices=1)
server.run(port=8000)

# Install and run
pip install litserve
python serve.py

# Test the endpoint
curl -X POST http://localhost:8000/predict \
  -H 'Content-Type: application/json' \
  -d '{"input": "Hello, world"}'

Related on TokRepo

• AI Tools for API -- Tools for building and serving AI APIs
• Featured Workflows -- Top-rated workflows on TokRepo

Common pitfalls

• The setup method runs once per worker; loading large models without specifying the device parameter wastes GPU memory
• Batching is enabled by default, which adds latency for single requests; disable it for low-latency single-request use cases
• GPU autoscaling requires proper CUDA setup; misconfigured drivers cause silent fallback to CPU