LitServe
Lightning-fast serving engine for any AI model of any size. Flexible. Easy. Enterprise-scale.
Stars: 2704
LitServe is a high-throughput serving engine designed for deploying AI models at scale. It generates an API endpoint for models, handles batching, streaming, and autoscaling across CPU/GPUs. LitServe is built for enterprise scale with a focus on minimal, hackable code-base without bloat. It supports various model types like LLMs, vision, time-series, and works with frameworks like PyTorch, JAX, Tensorflow, and more. The tool allows users to focus on model performance rather than serving boilerplate, providing full control and flexibility.
README:
Lightning-fast serving engine for AI models.
Easy. Flexible. Enterprise-scale.
LitServe is an easy-to-use, flexible serving engine for AI models built on FastAPI. It augments FastAPI with 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 due to AI-specific multi-worker handling.
✅ (2x)+ faster serving ✅ Easy to use ✅ LLMs, non LLMs and more ✅ Bring your own model ✅ PyTorch/JAX/TF/... ✅ Built on FastAPI ✅ GPU autoscaling ✅ Batching, Streaming ✅ Self-host or ⚡️ managed ✅ Compound AI ✅ Integrate with vLLM and more
Install LitServe via pip (more options):
pip install litserveThis toy example with 2 models (AI compound system) shows LitServe's flexibility (see real examples):
# server.py
import litserve as ls
# (STEP 1) - DEFINE THE API (compound AI system)
class SimpleLitAPI(ls.LitAPI):
def setup(self, device):
# setup is called once at startup. Build a compound AI system (1+ models), connect DBs, load data, etc...
self.model1 = lambda x: x**2
self.model2 = lambda x: x**3
def decode_request(self, request):
# Convert the request payload to model input.
return request["input"]
def predict(self, x):
# Easily build compound systems. Run inference and return the output.
squared = self.model1(x)
cubed = self.model2(x)
output = squared + cubed
return {"output": output}
def encode_response(self, output):
# Convert the model output to a response payload.
return {"output": output}
# (STEP 2) - START THE SERVER
if __name__ == "__main__":
# scale with advanced features (batching, GPUs, etc...)
server = ls.LitServer(SimpleLitAPI(), accelerator="auto", max_batch_size=1)
server.run(port=8000)Now run the server via the command-line
python server.pyRun the auto-generated test client:
python client.py Or use this terminal command:
curl -X POST http://127.0.0.1:8000/predict -H "Content-Type: application/json" -d '{"input": 4.0}'LitServe isn’t just for LLMs like vLLM or Ollama; it serves any AI model with full control over internals (learn more).
For easy LLM serving, integrate vLLM with LitServe, or use LitGPT (built on LitServe).
litgpt serve microsoft/phi-2
- LitAPI lets you easily build complex AI systems with one or more models (docs).
- Use the setup method for one-time tasks like connecting models, DBs, and loading data (docs).
- LitServer handles optimizations like batching, GPU autoscaling, streaming, etc... (docs).
- Self host on your own machines or use Lightning Studios for a fully managed deployment (learn more).
Learn how to make this server 200x faster.
Use LitServe to deploy any model or AI service: (Compound AI, Gen AI, classic ML, embeddings, LLMs, vision, audio, etc...)
Toy model: Hello world LLMs: Llama 3.2, LLM Proxy server, Agent with tool use RAG: vLLM RAG (Llama 3.2), RAG API (LlamaIndex) NLP: Hugging face, BERT, Text embedding API Multimodal: OpenAI Clip, MiniCPM, Phi-3.5 Vision Instruct, Qwen2-VL, Pixtral Audio: Whisper, AudioCraft, StableAudio, Noise cancellation (DeepFilterNet) Vision: Stable diffusion 2, AuraFlow, Flux, Image Super Resolution (Aura SR), Background Removal, Control Stable Diffusion (ControlNet) Speech: Text-speech (XTTS V2), Parler-TTS Classical ML: Random forest, XGBoost Miscellaneous: Media conversion API (ffmpeg), PyTorch + TensorFlow in one API, LLM proxy server
Browse 100+ community-built templates
State-of-the-art features:
✅ (2x)+ faster than plain FastAPI
✅ Bring your own model
✅ Build compound systems (1+ models)
✅ GPU autoscaling
✅ Batching
✅ Streaming
✅ Worker autoscaling
✅ Self-host on your machines
✅ Host fully managed on Lightning AI
✅ Serve all models: (LLMs, vision, etc.)
✅ Scale to zero (serverless)
✅ Supports PyTorch, JAX, TF, etc...
✅ OpenAPI compliant
✅ Open AI compatibility
✅ Authentication
✅ Dockerization
Note: We prioritize scalable, enterprise-level features over hype.
LitServe is designed for AI workloads. Specialized multi-worker handling delivers a minimum 2x speedup over FastAPI.
Additional features like batching and GPU autoscaling can drive performance well beyond 2x, scaling efficiently to handle more simultaneous requests than FastAPI and TorchServe.
Reproduce the full benchmarks here (higher is better).
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), integrate vLLM with LitServe, 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.
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 | ❌ | ✅ |
LitServe is a community project accepting contributions - Let's make the world's most advanced AI inference engine.
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for LitServe
Similar Open Source Tools
LitServe
LitServe is a high-throughput serving engine designed for deploying AI models at scale. It generates an API endpoint for models, handles batching, streaming, and autoscaling across CPU/GPUs. LitServe is built for enterprise scale with a focus on minimal, hackable code-base without bloat. It supports various model types like LLMs, vision, time-series, and works with frameworks like PyTorch, JAX, Tensorflow, and more. The tool allows users to focus on model performance rather than serving boilerplate, providing full control and flexibility.
openrl
OpenRL is an open-source general reinforcement learning research framework that supports training for various tasks such as single-agent, multi-agent, offline RL, self-play, and natural language. Developed based on PyTorch, the goal of OpenRL is to provide a simple-to-use, flexible, efficient and sustainable platform for the reinforcement learning research community. It supports a universal interface for all tasks/environments, single-agent and multi-agent tasks, offline RL training with expert dataset, self-play training, reinforcement learning training for natural language tasks, DeepSpeed, Arena for evaluation, importing models and datasets from Hugging Face, user-defined environments, models, and datasets, gymnasium environments, callbacks, visualization tools, unit testing, and code coverage testing. It also supports various algorithms like PPO, DQN, SAC, and environments like Gymnasium, MuJoCo, Atari, and more.
auto-news
Auto-News is an automatic news aggregator tool that utilizes Large Language Models (LLM) to pull information from various sources such as Tweets, RSS feeds, YouTube videos, web articles, Reddit, and journal notes. The tool aims to help users efficiently read and filter content based on personal interests, providing a unified reading experience and organizing information effectively. It features feed aggregation with summarization, transcript generation for videos and articles, noise reduction, task organization, and deep dive topic exploration. The tool supports multiple LLM backends, offers weekly top-k aggregations, and can be deployed on Linux/MacOS using docker-compose or Kubernetes.
inference
Xorbits Inference (Xinference) is a powerful and versatile library designed to serve language, speech recognition, and multimodal models. With Xorbits Inference, you can effortlessly deploy and serve your or state-of-the-art built-in models using just a single command. Whether you are a researcher, developer, or data scientist, Xorbits Inference empowers you to unleash the full potential of cutting-edge AI models.
Kiln
Kiln is an intuitive tool for fine-tuning LLM models, generating synthetic data, and collaborating on datasets. It offers desktop apps for Windows, MacOS, and Linux, zero-code fine-tuning for various models, interactive data generation, and Git-based version control. Users can easily collaborate with QA, PM, and subject matter experts, generate auto-prompts, and work with a wide range of models and providers. The tool is open-source, privacy-first, and supports structured data tasks in JSON format. Kiln is free to use and helps build high-quality AI products with datasets, facilitates collaboration between technical and non-technical teams, allows comparison of models and techniques without code, ensures structured data integrity, and prioritizes user privacy.
ChopperBot
A multifunctional, intelligent, personalized, scalable, easy to build, and fully automated multi platform intelligent live video editing and publishing robot. ChopperBot is a comprehensive AI tool that automatically analyzes and slices the most interesting clips from popular live streaming platforms, generates and publishes content, and manages accounts. It supports plugin DIY development and hot swapping functionality, making it easy to customize and expand. With ChopperBot, users can quickly build their own live video editing platform without the need to install any software, thanks to its visual management interface.
spandrel
Spandrel is a library for loading and running pre-trained PyTorch models. It automatically detects the model architecture and hyperparameters from model files, and provides a unified interface for running models.
kubesphere
KubeSphere is a distributed operating system for cloud-native application management, using Kubernetes as its kernel. It provides a plug-and-play architecture, allowing third-party applications to be seamlessly integrated into its ecosystem. KubeSphere is also a multi-tenant container platform with full-stack automated IT operation and streamlined DevOps workflows. It provides developer-friendly wizard web UI, helping enterprises to build out a more robust and feature-rich platform, which includes most common functionalities needed for enterprise Kubernetes strategy.
CodeGeeX4
CodeGeeX4-ALL-9B is an open-source multilingual code generation model based on GLM-4-9B, offering enhanced code generation capabilities. It supports functions like code completion, code interpreter, web search, function call, and repository-level code Q&A. The model has competitive performance on benchmarks like BigCodeBench and NaturalCodeBench, outperforming larger models in terms of speed and performance.
agentcloud
AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.
HAMi
HAMi is a Heterogeneous AI Computing Virtualization Middleware designed to manage Heterogeneous AI Computing Devices in a Kubernetes cluster. It allows for device sharing, device memory control, device type specification, and device UUID specification. The tool is easy to use and does not require modifying task YAML files. It includes features like hard limits on device memory, partial device allocation, streaming multiprocessor limits, and core usage specification. HAMi consists of components like a mutating webhook, scheduler extender, device plugins, and in-container virtualization techniques. It is suitable for scenarios requiring device sharing, specific device memory allocation, GPU balancing, low utilization optimization, and scenarios needing multiple small GPUs. The tool requires prerequisites like NVIDIA drivers, CUDA version, nvidia-docker, Kubernetes version, glibc version, and helm. Users can install, upgrade, and uninstall HAMi, submit tasks, and monitor cluster information. The tool's roadmap includes supporting additional AI computing devices, video codec processing, and Multi-Instance GPUs (MIG).
lance
Lance is a modern columnar data format optimized for ML workflows and datasets. It offers high-performance random access, vector search, zero-copy automatic versioning, and ecosystem integrations with Apache Arrow, Pandas, Polars, and DuckDB. Lance is designed to address the challenges of the ML development cycle, providing a unified data format for collection, exploration, analytics, feature engineering, training, evaluation, deployment, and monitoring. It aims to reduce data silos and streamline the ML development process.
auto-dev
AutoDev is an AI-powered coding wizard that supports multiple languages, including Java, Kotlin, JavaScript/TypeScript, Rust, Python, Golang, C/C++/OC, and more. It offers a range of features, including auto development mode, copilot mode, chat with AI, customization options, SDLC support, custom AI agent integration, and language features such as language support, extensions, and a DevIns language for AI agent development. AutoDev is designed to assist developers with tasks such as auto code generation, bug detection, code explanation, exception tracing, commit message generation, code review content generation, smart refactoring, Dockerfile generation, CI/CD config file generation, and custom shell/command generation. It also provides a built-in LLM fine-tune model and supports UnitEval for LLM result evaluation and UnitGen for code-LLM fine-tune data generation.
bee-agent-framework
The Bee Agent Framework is an open-source tool for building, deploying, and serving powerful agentic workflows at scale. It provides AI agents, tools for creating workflows in Javascript/Python, a code interpreter, memory optimization strategies, serialization for pausing/resuming workflows, traceability features, production-level control, and upcoming features like model-agnostic support and a chat UI. The framework offers various modules for agents, llms, memory, tools, caching, errors, adapters, logging, serialization, and more, with a roadmap including MLFlow integration, JSON support, structured outputs, chat client, base agent improvements, guardrails, and evaluation.
FlagEmbedding
FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: * **Long-Context LLM** : Activation Beacon * **Fine-tuning of LM** : LM-Cocktail * **Embedding Model** : Visualized-BGE, BGE-M3, LLM Embedder, BGE Embedding * **Reranker Model** : llm rerankers, BGE Reranker * **Benchmark** : C-MTEB
macai
Macai is a native macOS client for interacting with modern AI tools, such as ChatGPT and Ollama. It features organized chats with custom system messages, system-defined light/dark themes, backup and restore functionality, customizable context size, support for any model with a compatible API, formatted code blocks and tables, multiple chat tabs, CoreData data storage, streamed responses, and automatic chat name generation. Macai is in active development, with contributions welcome.
For similar tasks
ai-on-gke
This repository contains assets related to AI/ML workloads on Google Kubernetes Engine (GKE). Run optimized AI/ML workloads with Google Kubernetes Engine (GKE) platform orchestration capabilities. A robust AI/ML platform considers the following layers: Infrastructure orchestration that support GPUs and TPUs for training and serving workloads at scale Flexible integration with distributed computing and data processing frameworks Support for multiple teams on the same infrastructure to maximize utilization of resources
ray
Ray is a unified framework for scaling AI and Python applications. It consists of a core distributed runtime and a set of AI libraries for simplifying ML compute, including Data, Train, Tune, RLlib, and Serve. Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing ecosystem of community integrations. With Ray, you can seamlessly scale the same code from a laptop to a cluster, making it easy to meet the compute-intensive demands of modern ML workloads.
labelbox-python
Labelbox is a data-centric AI platform for enterprises to develop, optimize, and use AI to solve problems and power new products and services. Enterprises use Labelbox to curate data, generate high-quality human feedback data for computer vision and LLMs, evaluate model performance, and automate tasks by combining AI and human-centric workflows. The academic & research community uses Labelbox for cutting-edge AI research.
djl
Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. It is designed to be easy to get started with and simple to use for Java developers. DJL provides a native Java development experience and allows users to integrate machine learning and deep learning models with their Java applications. The framework is deep learning engine agnostic, enabling users to switch engines at any point for optimal performance. DJL's ergonomic API interface guides users with best practices to accomplish deep learning tasks, such as running inference and training neural networks.
mlflow
MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). MLflow's current components are:
* `MLflow Tracking
tt-metal
TT-NN is a python & C++ Neural Network OP library. It provides a low-level programming model, TT-Metalium, enabling kernel development for Tenstorrent hardware.
burn
Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.
awsome-distributed-training
This repository contains reference architectures and test cases for distributed model training with Amazon SageMaker Hyperpod, AWS ParallelCluster, AWS Batch, and Amazon EKS. The test cases cover different types and sizes of models as well as different frameworks and parallel optimizations (Pytorch DDP/FSDP, MegatronLM, NemoMegatron...).
For similar jobs
llm-resource
llm-resource is a comprehensive collection of high-quality resources for Large Language Models (LLM). It covers various aspects of LLM including algorithms, training, fine-tuning, alignment, inference, data engineering, compression, evaluation, prompt engineering, AI frameworks, AI basics, AI infrastructure, AI compilers, LLM application development, LLM operations, AI systems, and practical implementations. The repository aims to gather and share valuable resources related to LLM for the community to benefit from.
LitServe
LitServe is a high-throughput serving engine designed for deploying AI models at scale. It generates an API endpoint for models, handles batching, streaming, and autoscaling across CPU/GPUs. LitServe is built for enterprise scale with a focus on minimal, hackable code-base without bloat. It supports various model types like LLMs, vision, time-series, and works with frameworks like PyTorch, JAX, Tensorflow, and more. The tool allows users to focus on model performance rather than serving boilerplate, providing full control and flexibility.
how-to-optim-algorithm-in-cuda
This repository documents how to optimize common algorithms based on CUDA. It includes subdirectories with code implementations for specific optimizations. The optimizations cover topics such as compiling PyTorch from source, NVIDIA's reduce optimization, OneFlow's elementwise template, fast atomic add for half data types, upsample nearest2d optimization in OneFlow, optimized indexing in PyTorch, OneFlow's softmax kernel, linear attention optimization, and more. The repository also includes learning resources related to deep learning frameworks, compilers, and optimization techniques.
aiac
AIAC is a library and command line tool to generate Infrastructure as Code (IaC) templates, configurations, utilities, queries, and more via LLM providers such as OpenAI, Amazon Bedrock, and Ollama. Users can define multiple 'backends' targeting different LLM providers and environments using a simple configuration file. The tool allows users to ask a model to generate templates for different scenarios and composes an appropriate request to the selected provider, storing the resulting code to a file and/or printing it to standard output.
ENOVA
ENOVA is an open-source service for Large Language Model (LLM) deployment, monitoring, injection, and auto-scaling. It addresses challenges in deploying stable serverless LLM services on GPU clusters with auto-scaling by deconstructing the LLM service execution process and providing configuration recommendations and performance detection. Users can build and deploy LLM with few command lines, recommend optimal computing resources, experience LLM performance, observe operating status, achieve load balancing, and more. ENOVA ensures stable operation, cost-effectiveness, efficiency, and strong scalability of LLM services.
jina
Jina is a tool that allows users to build multimodal AI services and pipelines using cloud-native technologies. It provides a Pythonic experience for serving ML models and transitioning from local deployment to advanced orchestration frameworks like Docker-Compose, Kubernetes, or Jina AI Cloud. Users can build and serve models for any data type and deep learning framework, design high-performance services with easy scaling, serve LLM models while streaming their output, integrate with Docker containers via Executor Hub, and host on CPU/GPU using Jina AI Cloud. Jina also offers advanced orchestration and scaling capabilities, a smooth transition to the cloud, and easy scalability and concurrency features for applications. Users can deploy to their own cloud or system with Kubernetes and Docker Compose integration, and even deploy to JCloud for autoscaling and monitoring.
vidur
Vidur is a high-fidelity and extensible LLM inference simulator designed for capacity planning, deployment configuration optimization, testing new research ideas, and studying system performance of models under different workloads and configurations. It supports various models and devices, offers chrome trace exports, and can be set up using mamba, venv, or conda. Users can run the simulator with various parameters and monitor metrics using wandb. Contributions are welcome, subject to a Contributor License Agreement and adherence to the Microsoft Open Source Code of Conduct.
AI-System-School
AI System School is a curated list of research in machine learning systems, focusing on ML/DL infra, LLM infra, domain-specific infra, ML/LLM conferences, and general resources. It provides resources such as data processing, training systems, video systems, autoML systems, and more. The repository aims to help users navigate the landscape of AI systems and machine learning infrastructure, offering insights into conferences, surveys, books, videos, courses, and blogs related to the field.