Tools to Run AI Models Locally

Civitai is a self-hostable platform for sharing AI image models and artwork. Its Apache-2.0 code provides accounts, model uploads, browsing and comments. The public Civitai website also operates a hosted image generator, which is separate from the runnable local platform.

Bergamot Translator is a C++ library for running neural machine translation on your own device. Developers can integrate it into native applications or build it for WebAssembly. Separate browser integrations, such as the TranslateLocally extension, make the engine accessible to end users. The software is free and open source under the Mozilla Public License 2.0.

ColPali is a local AI document retrieval library for developers and researchers building document search or retrieval-augmented generation systems. It searches pages as images, using their text, charts and layout together rather than relying on a separate OCR pipeline. The colpali-engine package is deprecated; its maintainers recommend Sentence Transformers for new projects and production use.

AutoAWQ is a Python library for developers who want to compress and run LLMs on their own hardware using 4-bit Activation-aware Weight Quantization (AWQ). The project is archived and no longer maintained. It's open source under the MIT license. It installs as a Python package, with optional kernel or Intel CPU dependencies.

Backyard AI's desktop app uses llama.cpp, a runtime for running language models locally. It's for people looking for a desktop AI app on Windows or macOS, with separate Mac downloads for Intel and Apple Silicon hardware.

OGX, formerly Llama Stack, is a self-hosted AI application server for developers building chat apps, document search or AI agents. It brings model inference, file storage, vector search and agent orchestration into one process. You can run it on a laptop, in a datacenter or in the cloud. It's open source under MIT.

LlamaGPT is a self-hosted ChatGPT alternative for people who want general chat or coding help on their own computer or home server. It runs models locally and keeps conversation data on your device. After the initial model download, it works offline.

Layla is an offline AI assistant for Android and iOS that runs language models on your phone. It's for people who want private everyday chat or an AI companion with custom characters, memory and roleplay. Local conversations stay encrypted on your device; optional cloud mode sends requests to your chosen hosted provider.

Lollms WebUI is a local, single-user AI interface for people who want text chat and media generation in one place. It runs on Windows, macOS and Linux, with Docker support, and lets writers, developers and other users choose models and task-specific personalities. It's free and open source under Apache 2.0. The project receives minimal maintenance.

Sonar is a self-hosted inference engine for developers and teams serving Hugging Face-compatible language and multimodal models on their own hardware. Based on vLLM, it adds model and quantization formats, sampling methods, and deployment features. It's open source under AGPL-3.0.

Nexa SDK is an on-device AI inference framework for developers building applications that process text, images or audio on users' hardware. It runs models locally across CPUs, GPUs and NPUs, with a shared interface for different backends. Its scope includes language and vision models, speech recognition, speech synthesis and image generation.

SmolVLM is a compact vision language model from Hugging Face for developers building local AI applications that work with images and text. It can describe pictures, answer questions about diagrams, and read information from documents such as invoices. Its small memory footprint makes on-device use practical on laptops and smaller local setups.

MNN is a lightweight C++ framework for developers who want AI models to run on phones, PCs and embedded devices. It handles inference and training on the device, with a focus on small application footprints and hardware acceleration. The project is open source under Apache 2.0, and Alibaba uses it in apps including Taobao, Youku and DingTalk.

Kaito manages self-hosted LLM inference, fine-tuning, and document retrieval services in a Kubernetes cluster. It's for teams that want to run models on infrastructure they control while reducing the work of sizing GPU resources and managing model deployments. The project is open source under Apache 2.0.

Seldon Core 2 is an AI model serving framework for teams running production machine learning and LLM applications on Kubernetes. It can run on your own infrastructure or in a cloud environment. Its focus is managing individual models and connected applications within the same deployment system.

LLM Farm runs large language models offline on iOS and macOS. It's for people who want on-device AI chat or need to compare how different models perform on Apple hardware before choosing one for a project. The app is open source under the MIT license, with ggml and llama.cpp handling local inference.

MiniCPM-V is a family of local vision-language models for developers building apps that interpret images and video on their own hardware. It supports iOS, Android and HarmonyOS, as well as Mac deployment and server inference. The current repository states that MiniCPM-o/V code and model weights use Apache 2.0.

Parler-TTS generates speech locally, with text descriptions that control how the voice sounds. It's a Python library for developers building speech into applications and researchers who want to train or adapt a TTS model. The library uses the Apache 2.0 license and can run on CPU or CUDA GPUs, with support for Apple Silicon.

OpenCLIP is a Python and PyTorch library for developers and researchers who want to match images with text on their own hardware. It implements OpenAI's CLIP approach: images and descriptions become numerical representations that the model can compare. This supports image search and zero-shot classification, where text labels define the categories without a separate classifier trained for each task.

ggml is an open-source library for developers building software that runs machine learning models on their own hardware. It provides tensor computation in C/C++ without dependencies, with backends for CPUs, GPUs, NPUs and browsers. The project also documents the GGUF model file format.

Intel IPEX-LLM is a library for developers running or fine-tuning models on Intel hardware. The project is archived and no longer maintained. Intel reports known security issues and no longer accepts patches or provides updates. The code is open source under Apache 2.0.

ExLlamaV2 is a local LLM inference library for developers and people hosting models on their own consumer GPUs. ExLlamaV2 is archived and no longer maintained; development continues in ExLlamaV3. The V2 library is free and open source under the MIT license, runs on Windows and Linux, and uses NVIDIA GPUs through CUDA. It supports multiple GPUs.

Text Generation Inference (TGI) is a self-hosted LLM server for developers and teams serving models through an API on their own hardware. The repository is archived; its README describes maintenance mode and recommends other inference engines for new deployments. Its focus is handling concurrent generation requests and making efficient use of GPU memory.

NativeMind brings a local AI assistant into Chrome for people who want help with webpages, documents, and writing without sending that content to a cloud model. The browser extension connects to Ollama on your machine, keeping prompts and AI processing on-device. It requires no account and uses the AGPL-3.0 open-source license.

LiteRT is Google's open-source framework for developers building AI into apps that run on users' own devices. It succeeds TensorFlow Lite and covers model conversion, optimization and local inference. It's licensed under Apache 2.0.

ncnn is a C++ framework for developers building on-device AI into mobile, desktop and embedded applications. Its focus is running neural networks with a small memory footprint and no third-party runtime dependencies. Models run on the target device's CPU or a supported Vulkan GPU.

HQQ is a Python library that compresses language and vision models without needing a calibration dataset. It's for developers preparing models to run on their own hardware or servers, particularly when GPU memory limits the model they can use. The library is open source under Apache 2.0.

Private LLM runs AI chat entirely on your iPhone, iPad, or Mac. It's an app for people who want to use language models without sending their conversations to a cloud service. After the first model download, it works offline and requires no account. Conversations stay on-device, with no tracking or logs.

openWakeWord is a Python library for developers building voice interfaces that listen locally for a chosen word or phrase. It includes English models for triggers such as "hey jarvis" and "alexa", plus phrases for weather and timers. The code uses Apache 2.0. Included pretrained models use CC-BY-NC-SA-4.0, which restricts commercial use.

docTR is an open-source Python OCR library for developers building document processing tools and researchers comparing text recognition models. It reads PDFs and images on your own hardware, locating words and recognizing their text. The library uses PyTorch and carries the Apache 2.0 license.

ExecuTorch is PyTorch's runtime for developers building AI into mobile apps, desktop software and embedded devices. It runs models on the user's hardware, with support for Android, iOS, Linux, macOS and Windows, as well as microcontrollers. Developers can reuse a PyTorch model across targets, though hardware-specific deployments need their own exported model files.

Model2Vec turns sentence transformers into small static embedding models that run locally on CPU. It's for developers who need text embeddings for retrieval, code search or classification without the size and inference cost of the original transformer. The Python package is open source under the MIT license.

Colossal-AI is a Python framework for developers and researchers training or serving large AI models on their own GPU hardware. It addresses the memory and computing demands of models that are difficult to fit on a single GPU, with tools for distributing work across a cluster. It's open source under Apache 2.0.

GPTQModel is a Python toolkit for developers compressing LLMs and running them on their own hardware or servers. It brings model calibration, compression, quality checks and inference into one API, so teams can compare quantization methods without adopting a separate tool for each one.

MusicGen is Meta AI's music generation model within AudioCraft, a PyTorch library for developers and audio researchers who want to generate music in their own computing environment. It creates music from text descriptions and can use a melody to guide the result. AudioCraft includes both inference and training code, so it's suited to people building audio tools or studying music generation.

Moonshine is an on-device AI toolkit for developers building voice agents and applications that listen and speak. It combines speech to text, intent recognition and text to speech in one library. Voice processing stays on the device, and you don't need an account or API keys.