Awesome Production Machine Learning

This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning 🚀

You can keep up to date by watching this github repo to get a summary of the new production ML libraries added every month via releases 🤩

Additionally, we provide a search toolkit that helps you quickly navigate through the toolchain.

Quick links to sections on this page

Contributing to the list

Please review our CONTRIBUTING.md requirements when submitting a PR to help us keep the list clean and up-to-date - thank you to the community for supporting its steady growth 🚀

10 Min Video Overview

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Main Content

AutoML

• AIDE - AIDE is an open-source ML engineering agent that uses a tree search algorithm to autonomously explore, implement, and evaluate solution strategies for machine learning tasks.
• AutoGluon - Automated feature, model, and hyperparameter selection for tabular, image, and text data on top of popular machine learning libraries (Scikit-Learn, LightGBM, CatBoost, PyTorch, MXNet).
• Autokeras - AutoML library for Keras based on "Auto-Keras: Efficient Neural Architecture Search with Network Morphism".
• auto-sklearn - Framework to automate algorithm and hyperparameter tuning for sklearn.
• Ax - Ax is an accessible, general-purpose platform for understanding, managing, deploying, and automating adaptive experiments.
• BoTorch - BoTorch is a library for Bayesian Optimization built on PyTorch.
• EvalML - EvalML is an AutoML library which builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.
• Feature Engine - Feature-engine is a Python library that contains several transformers to engineer features for use in machine learning models.
• Featuretools - An open source framework for automated feature engineering.
• FLAML - FLAML is a fast library for automated machine learning & tuning.
• HEBO - Set of open-source hyperparameter optimization frameworks, including the winning submission to the NeurIPS 2020 Black-Box Optimisation Challenge tested on hyperparameter tuning tasks.
• Katib - A Kubernetes-based system for Hyperparameter Tuning and Neural Architecture Search.
• keras-tuner - Keras Tuner is an easy-to-use, distributable hyperparameter optimisation framework that solves the pain points of performing a hyperparameter search. Keras Tuner makes it easy to define a search space and leverage included algorithms to find the best hyperparameter values.
• Optuna - Optuna is an automatic hyperparameter optimisation software framework, particularly designed for machine learning.
• OSS Vizier - OSS Vizier is a Python-based service for black-box optimisation and research, one of the first hyperparameter tuning services designed to work at scale.
• Perpetual - A gradient boosting machine that doesn't need hyperparameter optimization, with a simple budget parameter to control model complexity.
• TPOT - Automation of sklearn pipeline creation (including feature selection, pre-processor, etc.).
• tsfresh - Automatic extraction of relevant features from time series.

Computation and Communication Optimisation

• Accelerate - Accelerate abstracts exactly and only the boilerplate code related to multi-GPU/TPU/mixed-precision and leaves the rest of your code unchanged.
• Adapters - Adapters is a unified library for parameter-efficient and modular transfer learning.
• BitBLAS - BitBLAS is a library to support mixed-precision BLAS operations on GPUs
• Cache-DiT - Cache-DiT is built on top of Diffusers and supports nearly all DiTs, providing hybrid cache acceleration (DBCache, TaylorSeer, SCM, etc.) and comprehensive parallelism optimizations including Context Parallelism, Tensor Parallelism, and hybrid 2D/3D parallelism, with compatibility for compilation, CPU offloading, and quantization.
• Colossal-AI - A unified deep learning system for big model era, which helps users to efficiently and quickly deploy large AI model training and inference.
• Composer - Composer is a PyTorch library that enables you to train neural networks faster, at lower cost, and to higher accuracy.
• CuDF - Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.
• CuML - cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects.
• CuPy - An implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it.
• DEAP - A novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data structures transparent. It works in perfect harmony with parallelisation mechanisms such as multiprocessing and SCOOP.
• DeepEP - DeepEP is a communication library tailored for Mixture-of-Experts (MoE) and expert parallelism (EP). It provides high-throughput and low-latency all-to-all GPU kernels, which are also known as MoE dispatch and combine. The library also supports low-precision operations, including FP8.
• DGL - DGL is an easy-to-use, high performance and scalable Python package for deep learning on graphs.
• DLRover - DLRover makes the distributed training of large AI models easy, stable, fast and green.
• Dask - Distributed parallel processing framework for Pandas and NumPy computations.
• DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
• FlagGems - FlagGems is a high-performance general operator library implemented in OpenAI Triton. It builds on a collection of backend neutral kernels that aims to accelerate LLM training and inference across diverse hardware platforms.
• Flashlight - A fast, flexible machine learning library written entirely in C++ from the Facebook AI Research and the creators of Torch, TensorFlow, Eigen and Deep Speech.
• Flax - A neural network library and ecosystem for JAX designed for flexibility.
• GPUStack - GPUStack is an open-source GPU cluster manager for running AI models.
• Hivemind - Decentralized deep learning in PyTorch.
• Horovod - Uber's distributed training framework for TensorFlow, Keras, and PyTorch.
• Jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more.
• Kompute - Blazing fast, lightweight and mobile phone-enabled Vulkan compute framework optimized for advanced GPU data processing usecases.
• Lava - Lava is an open source framework to develop applications for neuromorphic hardware architectures.
• Liger Kernel - Liger Kernel is a collection of Triton kernels designed specifically for LLM training.
• LightGBM - LightGBM is a gradient boosting framework that uses tree based learning algorithms.
• MLX - MLX is an array framework for machine learning on Apple silicon.
• Modin - Speed up your Pandas workflows by changing a single line of code.
• NVIDIA TensorRT - TensorRT is a C++ library for high-performance inference on NVIDIA GPUs and deep learning accelerators.
• Nevergrad - Nevergrad is a gradient-free optimisation platform.
• Norse - Norse aims to exploit the advantages of bio-inspired neural components, which are sparse and event-driven - a fundamental difference from artificial neural networks.
• Numba - A compiler for Python array and numerical functions.
• Optimum - Optimum is an extension of Transformers and Diffusers, providing a set of optimization tools enabling maximum efficiency to train and run models on targeted hardware while keeping things easy to use.
• PEFT - Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model's parameters.
• PaddlePaddle - PaddlePaddle is a framework to perform large-scale deep network training, using data sources distributed across hundreds of nodes.
• PyG - PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.
• PyTorch Lightning - PyTorch Lightning pretrains, finetunes and deploys AI models on multiple GPUs, TPUs with zero code changes.
• PyTorch - PyTorch is a library to develop and train neural network based deep learning models.
• Ray - Ray is a flexible, high-performance distributed execution framework for machine learning.
• SetFit - SetFit is an efficient and prompt-free framework for few-shot fine-tuning of Sentence Transformers.
• Sonnet - Sonnet is a library built on top of TensorFlow 2 designed to provide simple, composable abstractions for machine learning research.
• Streaming - A Data Streaming Library for Efficient Neural Network Training.
• TensorFlow - TensorFlow is a leading library designed for developing and deploying state-of-the-art machine learning applications.
• ThunderKittens ThunderKittens is a framework to make it easy to write fast deep learning kernels in CUDA.
• TorchOpt - TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
• Triton - Triton is a language and compiler for writing highly efficient custom Deep-Learning primitives. The aim of Triton is to provide an open-source environment to write fast code at higher productivity than CUDA, but also with higher flexibility than other existing DSLs.
• Vaex Vaex is a high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted).
• Vowpal Wabbit Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
• XGBoost - XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.
• YDF - YDF (Yggdrasil Decision Forests) is a library to train, evaluate, interpret, and serve Random Forest, Gradient Boosted Decision Trees, CART and Isolation forest models.
• bitsandbytes - Bitsandbytes library is a lightweight Python wrapper around CUDA custom functions, in particular 8-bit optimizers, matrix multiplication (LLM.int8()), and 8 & 4-bit quantization functions.
• einops - Flexible and powerful tensor operations for readable and reliable code.
• scikit-learn - Scikit-learn is a powerful machine learning library that provides a wide variety of modules for data access, data preparation and statistical model building.
• snnTorch - snnTorch is a deep and online learning library with spiking neural networks.
• torchdistill - torchdistill offers various state-of-the-art knowledge distillation methods and enables you to design (new) experiments simply by editing a declarative yaml config file instead of Python code.
• torchkeras The torchkeras library is a simple tool for training neural network in pytorch jusk in a keras style.
• veScale - veScale is a PyTorch native LLM training framework.
• yellowbrick - yellowbrick is a matplotlib-based model evaluation plots for scikit-learn and other machine learning libraries.

Data Annotation and Synthesis

• Argilla - Argilla helps domain experts and data teams to build better NLP datasets in less time.
• cleanlab - Python library for data-centric AI. Can automatically: find mislabeled data, detect outliers, estimate consensus + annotator-quality for multi-annotator datasets, suggest which data is best to (re)label next.
• COCO Annotator - Web-based image segmentation tool for object detection, localization and keypoints
• CVAT - CVAT (Computer Vision Annotation Tool) is OpenCV's web-based annotation tool for both videos and images for computer algorithms.
• Doccano - Open source text annotation tools for humans, providing functionality for sentiment analysis, named entity recognition, and machine translation.
• Gretel Synthetics - Gretel Synthetics is a synthetic data generators for structured and unstructured text, featuring differentially private learning.
• Label Studio - Multi-domain data labeling and annotation tool with standardized output format.
• LightlyStudio - An open source tool to curate, annotate, and manage vision datasets (images and videos). Supports embedding-based auto-selection, annotation, and auto-labeling for bounding boxes and segmentation.
• NeMo Curator - NeMo Curator is a GPU-accelerated framework for efficient large language model data curation.
• refinery - The data scientist's open-source choice to scale, assess and maintain natural language data.
• SDV - Synthetic Data Vault (SDV) is a Synthetic Data Generation ecosystem of libraries that allows users to easily learn single-table, multi-table and timeseries datasets to later on generate new Synthetic Data that has the same format and statistical properties as the original dataset.
• Semantic Segmentation Editor - Hitachi's Open source tool for labelling camera and LIDAR data.
• synthcity - synthcity is a library for generating and evaluating synthetic tabular data.
• TabGAN - Synthetic tabular data generation using GANs (CTGAN), Diffusion Models, and LLMs with adversarial filtering, privacy metrics, and sklearn integration.
• ViPE - ViPE is a spatial AI tool for annotating camera poses and dense depth maps from raw videos.
• YData Synthetic - YData Synthetic is a package to generate synthetic tabular and time-series data leveraging the state of the art generative models.

Data Pipeline

• Apache Airflow - Data Pipeline framework built in Python, including scheduler, DAG definition and a UI for visualisation.
• Apache Nifi - Apache NiFi was made for dataflow. It supports highly configurable directed graphs of data routing, transformation, and system mediation logic.
• Apache Oozie - Workflow scheduler for Hadoop jobs.
• Argo Workflows - Argo Workflows is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes. Argo Workflows is implemented as a Kubernetes CRD (Custom Resource Definition).
• Couler - Unified interface for constructing and managing machine learning workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.
• DataTrove - DataTrove is a library to process, filter and deduplicate text data at a very large scale.
• Dagster - A data orchestrator for machine learning, analytics, and ETL.
• DBT - ETL tool for running transformations inside data warehouses.
• Flyte - Lyft's Cloud Native Machine Learning and Data Processing Platform - (Demo).
• Genie - Job orchestration engine to interface and trigger the execution of jobs from Hadoop-based systems.
• Hamilton - Hamilton is a micro-orchestration framework for defining dataflows. Runs anywhere python runs (e.g. jupyter, fastAPI, spark, ray, dask). Brings software engineering best practices without you knowing it. Use it to define feature engineering transforms, end-to-end model pipelines, and LLM workflows. It complements macro-orchestration systems (e.g. kedro, luigi, airflow, dbt, etc.) as it replaces the code within those macro tasks. Comes with a self-hostable UI that captures lineage & provenance, execution telemetry & data summaries, and builds a self-populating catalog; usable in development as well as production.
• Instill VDP - Instill VDP (Versatile Data Pipeline) aims to streamline the data processing pipelines from inception to completion.
• Instructor - Instructor makes it easy to get structured data like JSON from LLMs like GPT-3.5, GPT-4, GPT-4-Vision, and open-source models.
• Kedro - Kedro is a workflow development tool that helps you build data pipelines that are robust, scalable, deployable, reproducible and versioned.
• Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs, handling dependency resolution, workflow management, visualisation, etc..
• Metaflow - A framework for data scientists to easily build and manage real-life data science projects.
• Pachyderm - Open source distributed processing framework build on Kubernetes focused mainly on dynamic building of production machine learning pipelines - (Video).
• Ploomber - The fastest way to build data pipelines. Develop iteratively, deploy anywhere.
• Pixeltable – Open-source Python library providing declarative, incremental data infrastructure for building and managing multimodal AI workloads.
• Prefect Core - Workflow management system that makes it easy to take your data pipelines and add semantics like retries, logging, dynamic mapping, caching, failure notifications, and more.
• SeqIO - SeqIO is a library for processing sequential data to be fed into downstream sequence models.
• Snakemake - Workflow management system for reproducible and scalable data analyses.
• Towhee - General-purpose machine learning pipeline for generating embedding vectors using one or many ML models.
• unstructured - unstructured streamlines and optimizes the data processing workflow for LLMs, ingesting and pre-processing images and text documents, such as PDFs, HTML, Word docs, and many more.
• ZenML - ZenML is an extensible, open-source MLOps framework to create reproducible ML pipelines with a focus on automated metadata tracking, caching, and many integrations to other tools.

Data Science Notebook

• Apache Zeppelin - Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more.
• Deepnote - Deepnote is a drop-in replacement for Jupyter with an AI-first design, sleek UI, new blocks, and native data integrations. Use Python, R, and SQL locally in your favorite IDE, then scale to Deepnote cloud for real-time collaboration, Deepnote agent, and deployable data apps.
• Jupyter Notebooks - Web interface python sandbox environments for reproducible development
• Marimo - Reactive Python notebook — run reproducible experiments, execute as a script, deploy as an app, and version with git.
• Papermill - Papermill is a library for parameterizing notebooks and executing them like Python scripts.
• Polynote - Polynote is an experimental polyglot notebook environment. Currently, it supports Scala and Python (with or without Spark), SQL, and Vega.
• RMarkdown - The rmarkdown package is a next generation implementation of R Markdown based on Pandoc.
• Stencila - Stencila is a platform for creating, collaborating on, and sharing data driven content. Content that is transparent and reproducible.
• Voilà - Voilà turns Jupyter notebooks into standalone web applications that can e.g. be used as dashboards.
• .NET Interactive - .NET Interactive takes the power of .NET and embeds it into your interactive experiences.

Data Storage Optimisation

• AIStore - AIStore is a lightweight object storage system with the capability to linearly scale out with each added storage node and a special focus on petascale deep learning.
• Alluxio - A virtual distributed storage system that bridges the gab between computation frameworks and storage systems.
• Apache Arrow - In-memory columnar representation of data compatible with Pandas, Hadoop-based systems, etc..
• Apache Druid - A high performance real-time analytics database. Check this article for introduction.
• Apache Hudi - Hudi is a transactional data lake platform that brings core warehouse and database functionality directly to a data lake. Hudi is great for streaming workloads, and also allows creation of efficient incremental batch pipelines. Supports popular query engines including Spark, Flink, Presto, Trino, Hive, etc. More info here.
• Apache Iceberg - Iceberg is an ACID-compliant, high-performance format built for huge analytic tables (containing tens of petabytes of data), and it brings the reliability and simplicity of SQL tables to big data, while making it possible for engines like Spark, Trino, Flink, Presto, Hive and Impala to safely work with the same tables, at the same time. More info here.
• Apache Ignite - A memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale - Demo.
• Apache Parquet - On-disk columnar representation of data compatible with Pandas, Hadoop-based systems, etc..
• Apache Pinot - A realtime distributed OLAP datastore. Comparison of the open source OLAP systems for big data: ClickHouse, Druid, and Pinot is found here.
• Casibase - Casibase is a LangChain-like RAG (Retrieval-Augmented Generation) knowledge database with web UI and Enterprise SSO.
• Chroma - Chroma is an open-source embedding database.
• ClickHouse - ClickHouse is an open source column oriented database management system.
• Delta Lake - Delta Lake is a storage layer that brings scalable, ACID transactions to Apache Spark and other big-data engines.
• EdgeDB - Gel supercharges Postgres with a modern data model, graph queries, Auth & AI solutions, and much more.
• GPTCache - GPTCache is a library for creating semantic cache for large language model queries.
• InfluxDB Scalable datastore for metrics, events, and real-time analytics.
• Milvus Milvus is a cloud-native, open-source vector database built to manage embedding vectors generated by machine learning models and neural networks.
• Marqo Marqo is an end-to-end vector search engine.
• pgvector pgvector helps with vector similarity search for Postgres.
• PostgresML PostgresML is a machine learning extension for PostgreSQL that enables you to perform training and inference on text and tabular data using SQL queries.
• Redis Redis is an open-source, in-memory data store that supports vector similarity search, making it suitable for AI/ML applications such as semantic search and recommendation systems.
• Safetensors Simple, safe way to store and distribute tensors.
• TimescaleDB An open-source time-series SQL database optimized for fast ingest and complex queries packaged as a PostgreSQL extension - (Video).
• Weaviate - A low-latency vector search engine (GraphQL, RESTful) with out-of-the-box support for different media types. Modules include Semantic Search, Q&A, Classification, Customizable Models (PyTorch/TensorFlow/Keras), and more.
• Zarr - Python implementation of chunked, compressed, N-dimensional arrays designed for use in parallel computing.

Data Stream Processing

• Apache Beam Apache Beam is a unified programming model for Batch and Streaming.
• Apache Flink - Open source stream processing framework with powerful stream and batch processing capabilities.
• Apache Kafka - Kafka client library for building applications and microservices where the input and output are stored in kafka clusters.
• Apache Samza - Distributed stream processing framework. It uses Apache Kafka for messaging, and Apache Hadoop YARN to provide fault tolerance, processor isolation, security, and resource management.
• Apache Spark - Micro-batch processing for streams using the apache spark framework as a backend supporting stateful exactly-once semantics.
• Bytewax - Flexible Python-centric stateful stream processing framework built on top of Rust engine.
• FastStream - A modern broker-agnostic streaming Python framework supporting Apache Kafka, RabbitMQ and NATS protocols, inspired by FastAPI and easily integratable with other web frameworks.
• MOA - MOA (Massive Online Analysis) is an open source framework for Big Data stream mining.
• MosaicML Streaming - Fast, deterministic streaming of large datasets from cloud storage for distributed model training.
• RisingWave - A distributed SQL streaming database that unifies stream processing and low-latency serving, ideal for building and serving features for online machine learning.
• TensorStore - Library for reading and writing large multi-dimensional arrays.

Deployment and Serving

• Agenta - Agenta provides end-to-end tools for the entire LLMOps workflow: building (LLM playground, evaluation), deploying (prompt and configuration management), and (LLM observability and tracing).
• AirLLM - AirLLM optimizes inference memory usage, allowing 70B large language models to run inference on a single 4GB GPU card without quantization, distillation and pruning.
• AITemplate - AITemplate (AIT) is a Python framework that transforms deep neural networks into CUDA (NVIDIA GPU) / HIP (AMD GPU) C++ code for lightning-fast inference serving.
• BentoML - BentoML is an open source framework for high performance ML model serving.
• BISHENG - BISHENG is an open LLM application devops platform, focusing on enterprise scenarios.
• DeepDetect - Machine Learning production server for TensorFlow, XGBoost and Cafe models written in C++ and maintained by Jolibrain.
• Dynamo - NVIDIA Dynamo is a high-throughput, low-latency inference framework designed for serving generative AI and reasoning models in multi-node distributed environments.
• exo - exo helps you run your AI cluster at home with everyday devices.
• Genkit - Genkit is an open source framework for building AI-powered apps with familiar code-centric patterns. Genkit makes it easy to develop, integrate, and test AI features with observability and evaluations.
• GoModel - GoModel is a self-hosted AI gateway written in Go that exposes a unified OpenAI-compatible API across OpenAI, Anthropic, Gemini, Groq, xAI, Ollama and other providers, with routing, usage tracking, rate limits, and guardrails.
• Inference - A fast, production-ready inference server for computer vision supporting deployment of many popular model architectures and fine-tuned models. With Inference, you can deploy models such as YOLOv5, YOLOv8, CLIP, SAM, and CogVLM on your own hardware using Docker.
• Infinity - Infinity is a high-throughput, low-latency REST API for serving text-embeddings, reranking models and clip.
• IPEX-LLM - IPEX-LLM is a PyTorch library for running LLM on Intel CPU and GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max) with very low latency.
• LiteLLM - LiteLLM is a Python SDK, Proxy Server (LLM Gateway) to call 100+ LLM APIs in OpenAI format - Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, Replicate, Groq.
• LiteRT - LiteRT (formerly TensorFlow Lite) is Google's high-performance runtime for on-device AI inference, enabling deployment of machine learning models on mobile, embedded, and edge devices.
• LiteRT-LM - LiteRT-LM is Google's production-ready, high-performance inference framework for deploying Large Language Models on edge devices, with cross-platform support for Android, iOS, Web, Desktop, and IoT.
• LitServe - LitServe is a flexible serving engine for AI models built on FastAPI. It supports custom inference engines for models, agents, multi-modal systems, RAG, and complex ML pipelines.
• Jina-serve - Jina-serve is a framework for building and deploying AI services that communicate via gRPC, HTTP and WebSockets.
• Kiln - Kiln is an OSS tool for fine-tuning LLM models, synthetic data generation, and collaborating on datasets.
• KServe - KServe provides a Kubernetes Custom Resource Definition for serving predictive and generative ML.
• KTransformers - KTransformers is a flexible framework for experiencing cutting-edge LLM inference optimizations.
• Langtrace - Langtrace is an open-source, Open Telemetry based end-to-end observability tool for LLM applications, providing real-time tracing, evaluations and metrics for popular LLMs, LLM frameworks, vectorDBs and more.
• Lepton AI - LeptonAI Python library allows you to build an AI service from Python code with ease.
• LightLLM - LightLLM is a Python-based LLM (Large Language Model) inference and serving framework, notable for its lightweight design, easy scalability, and high-speed performance.
• llama.cpp - llama.cpp is an open source software library that performs inference on various large language models such as Llama.
• llmfit - A terminal tool that right-sizes LLM models to your system's RAM, CPU, and GPU. Detects your hardware, scores each model across quality, speed, fit, and context dimensions, and tells you which ones will actually run well on your machine.
• LMCache - LMCache is a high-performance KV cache layer that accelerates LLM inference.
• LMDeploy - LMDeploy is a toolkit for compressing, deploying, and serving LLM.
• LM Studio - LM Studio is a tool for deploying LLM models locally on the computer, even on a relatively modest machine, provided it meets the minimum requirements.
• LocalAI - LocalAI is a drop-in replacement REST API that's compatible with OpenAI API specifications for local inferencing.
• MindsDB - MindsDB is the platform to create, serve, and fine-tune models in real-time from your database, vector store, and application data.
• mini-sglang - mini-sglang is a lightweight and efficient serving framework for large language models.
• MLRun- MLRun is an open MLOps framework for quickly building and managing continuous ML and generative AI applications across their lifecycle.
• MLServer - An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more.
• Model Runner - Docker Model Runner makes it easy to manage, run, and serve AI models using Docker, supporting LLMs and other AI models pulled directly from Docker Hub or any OCI-compliant registry.
• Mosec - A rust-powered and multi-stage pipelined model server which offers dynamic batching and more. Super easy to implement and deploy as micro-services.
• nano-vllm - nano-vllm is a lightweight vLLM implementation built from scratch, providing fast offline inference with optimization techniques such as prefix caching, tensor parallelism, and CUDA graph.
• nndeploy - An Easy-to-Use and High-Performance AI deployment framework.