LitServe: Easily serve AI models Lightning fast ⚡

Flexible, high-throughput serving engine for AI models.
Friendly interface. Enterprise scale.

LitServe is a flexible serving engine for AI models built on FastAPI. Features like batching, streaming, and GPU autoscaling eliminate the need to rebuild a FastAPI server per model.

LitServe is at least 2x faster than plain FastAPI.

✅ (2x)+ faster serving   ✅ Self-host or fully managed  ✅ Auto-GPU, multi-GPU   
✅ Multi-modal            ✅ PyTorch/JAX/TF              ✅ Full control          
✅ Batching               ✅ Built on Fast API           ✅ Streaming

Quick start • Examples • Features • Performance • Hosting • Docs

Quick start

Install LitServe via pip (other install options):

pip install litserve

Define a server

Here's a hello world example (explore real examples):

# server.py
import litserve as ls

# STEP 1: DEFINE A MODEL API
class SimpleLitAPI(ls.LitAPI):
    # Called once at startup. Setup models, DB connections, etc...
    def setup(self, device):
        self.model = lambda x: x**2  

    # Convert the request payload to model input.
    def decode_request(self, request):
        return request["input"] 

    # Run inference on the the model, return the output.
    def predict(self, x):
        return self.model(x) 

    # Convert the model output to a response payload.
    def encode_response(self, output):
        return {"output": output} 

# STEP 2: START THE SERVER
if __name__ == "__main__":
    api = SimpleLitAPI()
    server = ls.LitServer(api, accelerator="auto")
    server.run(port=8000)

Now run the server via the command-line

python server.py

LitAPI class gives full control and hackability.
LitServer handles optimizations like batching, auto-GPU scaling, etc...

Query the server

Use the automatically generated LitServe client:

python client.py

Write a custom client

import requests
response = requests.post(
    "http://127.0.0.1:8000/predict",
    json={"input": 4.0}
)

Featured examples

Use LitServe to deploy any model or AI service: (Gen AI, classical ML, embedding servers, LLMs, vision, audio, multi-modal systems, etc...)

litserve-overview-v2.mp4

Featured examples

Toy model:      Hello world
LLMs:           Llama 3 (8B), LLM Proxy server
NLP:            Hugging face, BERT, Text embedding API
Multimodal:     OpenAI Clip, MiniCPM, Chameleon 30B
Audio:          Whisper, AudioCraft, StableAudio, Noise cancellation (DeepFilterNet)
Vision:         Stable diffusion 2, AuroraFlow, Flux, Image super resolution (Aura SR)
Speech:         Text-speech (XTTS V2)
Classical ML:   Random forest, XGBoost
Miscellaneous:  Media conversion API (ffmpeg)

Browse 100s of community-built templates.

Features

LitServe supports multiple advanced state-of-the-art features.

✅ (2x)+ faster serving than plain FastAPI
✅ Self host on your own machines
✅ Host fully managed on Lightning AI
✅ Serve all models: LLMs, vision, time series, etc...
✅ Auto-GPU scaling
✅ Authentication
✅ Autoscaling
✅ Batching
✅ Streaming
✅ Scale to zero (serverless)
✅ All ML frameworks: PyTorch, Jax, Tensorflow, Hugging Face...
✅ Open AI compatibility

10+ features...

Note: Our goal is not to jump on every hype train, but instead support features that scale under the most demanding enterprise deployments.

Performance

LitServe is highly optimized for parallel execution with native features optimized to scale AI workloads. Our benchmarks show that LitServe (built on FastAPI) handles more simultaneous requests than FastAPI and TorchServe (higher is better).

Reproduce the full benchmarks here.

These results are for image and text classification ML tasks. The performance relationships hold for other ML tasks (embedding, LLM serving, audio, segmentation, object detection, summarization etc...).

💡 Note on LLM serving: For high-performance LLM serving (like Ollama/VLLM), use LitGPT or build your custom VLLM-like server with LitServe. Optimizations like kv-caching, which can be done with LitServe, are needed to maximize LLM performance.

Hosting options

LitServe can be hosted independently on your own machines or fully managed via Lightning Studios.

Self-hosting is ideal for hackers, students, and DIY developers, while fully managed hosting is ideal for enterprise developers needing easy autoscaling, security, release management, and 99.995% uptime and observability.

Feature	Self Managed	Fully Managed on Studios
Deployment	✅ Do it yourself deployment	✅ One-button cloud deploy
Load balancing	❌	✅
Autoscaling	❌	✅
Scale to zero	❌	✅
Multi-machine inference	❌	✅
Authentication	❌	✅
Own VPC	❌	✅
AWS, GCP	❌	✅
Use your own cloud commits	❌	✅

Community

LitServe is a community project accepting contributions - Let's make the world's most advanced AI inference engine.

💬 Get help on Discord
📋 License: Apache 2.0

Frenz86/LitServe