model-serving

A high-throughput and memory-efficient inference and serving engine for LLMs

The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!

A framework for efficient model inference with omni-modality models

Standardized Distributed Generative and Predictive AI Inference Platform for Scalable, Multi-Framework Deployment on Kubernetes

Open-Source Personal Cloud OS for Always-On Agents

In this repository, I will share some useful notes and references about deploying deep learning-based models in production.

🚀 Awesome System for Machine Learning ⚡️ AI System Papers and Industry Practice. ⚡️ System for Machine Learning, LLM (Large Language Model), GenAI (Generative AI). 🍻 OSDI, NSDI, SIGCOMM, SoCC, MLSys, etc. 🗃️ Llama3, Mistral, etc. 🧑‍💻 Video Tutorials.

LightLLM is a Python-based LLM (Large Language Model) inference and serving framework, notable for its lightweight design, easy scalability, and high-speed performance.

FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, TensorOpera AI (https://TensorOpera.ai) is your generative AI platform at scale.

Multi-LoRA inference server that scales to 1000s of fine-tuned LLMs

High-performance inference framework for large language models, focusing on efficiency, flexibility, and availability.

Community maintained hardware plugin for vLLM on Ascend

OpenLake is a high performance storage engine for efficient LLM inference and GPU Training

🏕️ Reproducible development environment for humans and agents

AICI: Prompts as (Wasm) Programs

MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.

An open source DevOps tool from the CNCF for packaging and versioning AI/ML models, datasets, code, and configuration into an OCI Artifact.

RTP-LLM: Alibaba's high-performance LLM inference engine for diverse applications.

Hopsworks - Data-Intensive AI platform with a Feature Store

The simplest way to serve AI/ML models in production

SGLang-Omni is a high-performance serving framework for audio models (TTS, ASR) and unified multimodal models.

A throughput-oriented high-performance serving framework for LLMs

A scalable inference server for models optimized with OpenVINO™

A highly optimized LLM inference acceleration engine for Llama and its variants.

A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine

Serverless LLM Serving for Everyone.

Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference.

Pure Rust + CUDA LLM inference engine — no PyTorch, OpenAI-compatible, serves Qwen3 to Kimi-K2

FastAPI Skeleton App to serve machine learning models production-ready.