Top 10 Free Local Neural Networks: How to Download and Install AI on Your Computer

Stable Diffusion web UI — a powerful image generator with flexible settings
InvokeAI — a convenient image editor with a modular structure
GPT4All — a simple assistant with a catalog of popular language models
KoboldAI — a generator of interactive stories and role-playing texts
LM Studio — a universal platform for launching popular LLMs
Whisper.cpp — fast offline speech-to-text translation
Ollama — a simple model manager with a catalog and API
Text Generation Web UI — a browser interface for local chatbots
ComfyUI — a modular constructor for creating node-based images
DeepFaceLab — a popular open-source neural network for deepfakes
Advantages and limitations of local neural networks
An example of running a local neural network on a laptop

Learn for free: "Neural Networks. A Practical Course"

A local neural network is an artificial intelligence model that runs directly on your computer without an internet connection. For example, you can download the LLaMA text model and use it on your laptop to generate texts without sending data to cloud services. This provides greater security and privacy, since all information remains on your device. Local neural networks are becoming increasingly popular, allowing users to quickly and easily create content without relying on external resources. This review presents ten free neural networks that will be relevant in 2025. You'll find brief descriptions, links to documentation, and the ability to download the necessary files. We'll also discuss the advantages and disadvantages of local models, determine who they're ideal for, and suggest additional options if the selection provided proves insufficient. This will help you choose the most suitable tools for your tasks in the field of artificial intelligence.

Stable Diffusion web UI - a powerful image generator with flexible settings
InvokeAI - a convenient image editor with a modular structure
GPT4All - a simple assistant with a catalog of popular language models
KoboldAI - a generator of interactive stories and role-playing texts
LM Studio - a universal platform for launching popular LLM
Whisper.cpp - fast offline speech-to-text translation
Ollama - a simple model manager with a catalog and API
Text Generation Web UI - a browser interface for local chatbots
ComfyUI - a modular designer for creating images based on nodes
DeepFaceLab - a popular open-source neural network for deepfakes
Advantages and Limitations Local neural networks
Example of running a local neural network on a laptop

We have provided approximate system requirements for all neural networks. Actual resource consumption varies depending on the specific model, its size, and the complexity of the tasks performed. These requirements will help you better understand what parameters are necessary for the effective operation of neural networks.

It is important to remember that the specified disk space applies only to the software and does not take into account the space required to store the models. For example, an application may take up 200 MB, while a model can take up 7 to 15 GB or even more. Therefore, when planning storage space, you should also consider the size of the models to avoid running out of disk space.

Stable Diffusion web UI — a powerful image generator with flexible settings

System requirements for optimal performance include a video card with at least 4 GB of memory. However, for training and running resource-intensive models, we recommend using video cards with 6 to 8 GB of memory. Python version 3.10.6 and the Git version control system are required for full functionality. Windows, Linux, and macOS operating systems are supported, providing flexibility and accessibility for users across platforms.

To install the application, download the repository from GitHub and run the webui-user.bat script for Windows or webui.sh for Linux and macOS. Make sure you have all the necessary dependencies installed for correct operation.

Documentation on the GitHub Wiki platform allows users to create, edit, and share project information. GitHub Wiki is convenient for building a knowledge base, allowing teams to organize their documents and share useful resources.

Users can easily add new pages, edit existing ones, and organize information hierarchically. The Wiki supports Markdown, making it easy to format text and add images. This makes the documentation creation process more intuitive and accessible.

GitHub Wiki also supports versioning, allowing you to track changes and revert to previous versions of documentation. This is especially useful for teams working on projects in a constantly changing environment.

Using GitHub Wiki promotes team transparency and simplifies access to necessary information, which ultimately improves the quality of project work and contributes to their successful implementation.

Stable Diffusion web UI is a convenient browser interface for working with Stable Diffusion models. With it, users can create unique images from scratch, edit existing images based on text descriptions, draw elements outside the original image, perform spot correction, colorize black-and-white sketches, and increase the resolution of images without significant loss of quality. This tool allows you to significantly expand the capabilities of graphic design and artificial intelligence, making the process of creating and editing visual content more accessible and efficient.

Screenshot: Stable Diffusion web UI

InvokeAI — a convenient image editor with a modular

System requirements for installing the program include Windows 10 and above, macOS 14 and above, and Linux (starting with Ubuntu 20.04). A NVIDIA 10xx series or newer graphics card with at least 4 GB of video memory is required. At least 8 GB of RAM and at least 10 GB of free disk space for the basic installation are required for proper operation. An additional 15 to 30 GB is required for storing models. On Mac devices with Apple Silicon processors, at least 16 GB of RAM is required. AMD graphics card support is available only in the Linux operating system.
To install, download the launcher corresponding to your operating system and follow the provided instructions. This will ensure the program is configured and runs correctly on your device.
The Invoke website documentation provides a complete guide to using and configuring the platform. Here you will find information about the functionality, integration options, and recommendations for optimizing the site experience.
The Invoke platform offers users an intuitive interface, making it easy to navigate and interact with content. The documentation provides step-by-step instructions for content creation, project management, and data analysis.
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The documentation is constantly updated to reflect new features and improvements, as well as to provide users with relevant tips and best practices. Using this information, you can get the most out of the Invoke platform and achieve your goals.
InvokeAI is a powerful application designed for creating and editing visual content. It offers a node-based system that allows for flexible customization of the image generation process. The application offers a workspace with a variety of ready-made templates designed for various tasks, such as image upscaling, graphics editing, and content generation from text descriptions. This makes InvokeAI the ideal tool for both professionals and amateurs seeking to create high-quality visual content.
InvokeAI offers support for a wide range of models from the Stable Diffusion 1.5, Stable Diffusion XL, and Flux families. Model installation is handled through a built-in manager, making the process simple and convenient. For example, you can integrate the Dreamshaper v7 neural network, which is designed for generating photorealistic images, or use Deliberate to create detailed artwork. This allows users to choose the optimal tools for their creative tasks and improves the quality of the content they create.

Screenshot: Invoke

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Top 12 Free Neural Networks for Image Generation and Editing

In today's world, artificial intelligence technologies are rapidly developing, and neural networks are becoming an essential tool for creating and editing images. This article presents a list of twelve free neural networks that will help you create unique visual materials. These tools are suitable for both professionals and amateurs, providing a wide range of possibilities for working with graphics.

Many of the listed neural networks have an intuitive interface and allow users to easily generate images based on text descriptions or edit existing photos. Using these free resources, you can improve the quality of your visual projects and expand your creative horizons.

Each of the neural networks in this list has its own features and functionality, allowing you to choose the right tool depending on your needs. Whether you create content for social media, web design, or advertising materials, these neural networks will help you achieve impressive results.

Explore the capabilities of each of these neural networks and find the one that will become your reliable assistant in the world of digital art.

GPT4All - a simple assistant with a catalog of popular language models

System requirements for running the software include a processor that supports AVX or AVX2, and at least 1.7 GB of free hard disk space. Additional requirements depend on specific neural network models, which can require anywhere from 3 to 8 GB of memory or more. Ensure your hardware meets these standards for optimal performance.

To install the program, download the setup file and follow the instructions provided.

The GPT4All website documentation is a comprehensive resource dedicated to the platform's functionality and capabilities. On the site, you will find detailed instructions on using GPT-4, as well as recommendations for integrating the model into various applications. We strive to provide users with up-to-date information on the latest updates, new features, and methods for optimizing GPT-4. All documentation is structured for easy searching and quick access to the data you need. Regardless of your skill level, you will find useful materials to help you get the most out of GPT4All.

GPT4All is a feature-rich application that supports a variety of neural networks, including Llama, DeepSeek, and Hermes, as well as about 20 other models. All available models can be easily downloaded through an intuitive interface. If your system doesn't meet the minimum requirements, a warning will appear below the download button. For example, to run the Llama2-7B model stably, you need at least 8 GB of RAM and a processor that supports AVX2 instructions. Using GPT4All allows you to effectively work with various neural networks, providing users with convenient access to modern machine learning technologies.

Screenshot: Nomic Blog

KoboldAI — generator of interactive stories and role-playing texts

System requirements: for installation The environment requires a minimum of 8 GB of RAM and approximately 20 GB of free disk space (excluding models). To increase generation speed, an NVIDIA graphics card with Compute Capability 5.0 or higher or a compatible AMD graphics card is required (only on Linux operating systems with ROCm).

To install KoboldAI, download the client from the official website and follow the provided instructions. Please note that on Windows, the installer may create a temporary virtual drive. This drive disappears after a computer reboot, but is automatically restored the next time the program is launched. Make sure you follow all steps to successfully configure the application.

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KoboldAI offers AI-Dungeon Adventure mode—an interactive text quest inspired by AI Dungeon 2. In this mode, you can initiate any action, such as "I enter a dark forest," and the neural network will generate a continuation with a detailed description of the environment and possible events. This approach allows users to immerse themselves in unique adventures, experiencing a new and exciting storyline each time. KoboldAI enables a high degree of interaction and creativity, allowing you to become the author of your story.

Frame: A TO Z AI TECH / YouTube

LM Studio — a universal platform for running popular LLMs

System requirements for the platform to function correctly include a minimum of 16 GB of RAM and at least 20 GB of free disk space, regardless of model. The platform supports Windows (x64 and ARM64), macOS (both Apple Silicon and Intel), and Linux (x64). Ensuring these conditions will optimize performance and improve the user experience.

To install LM Studio, download the client from the developer's official website. Make sure you select the latest version to access all new features and fixes. After downloading, follow the on-screen instructions to complete the installation.

The LM Studio documentation is an essential resource, providing users with the information they need to effectively use the platform. Within this documentation, you'll find detailed instructions, guides, and tips on all aspects of LM Studio. We strive to provide relevant and understandable materials to simplify the learning process and optimize your productivity. Explore the documentation to get the most out of LM Studio and achieve your goals.

LM Studio is software for running powerful language models locally. It offers an intuitive interface and direct access to a vast library of Hugging Face models. With LM Studio, you can quickly load models like Llama 3.1 and use them for a variety of tasks, including coding, word processing, and more. This solution is ideal for developers and researchers who want to work effectively with modern language technologies.

Screenshot: LM Studio

Whisper.cpp — fast offline speech-to-text translation

System requirements include a minimum of 8–16 GB RAM, which depends on the size of the selected neural network. At least 5 GB of free disk space is also required to store models. Hardware acceleration technologies such as CPU and GPU are supported. This includes CUDA for NVIDIA graphics cards, as well as Vulkan, OpenVINO, and ROCm for AMD, among other backends. Meeting these requirements will significantly improve the efficiency of working with neural networks.

To install the project, you need to clone the repository from GitHub, then download the corresponding model in GGML format. After that, build the project using CMake. Following these steps, you will be able to successfully deploy the project and start using it.

The Whisper.cpp project documentation is a detailed guide that will help users understand the main features and functionality of this library. Whisper.cpp is designed to process and analyze audio data, ensuring high-accuracy speech recognition. The README file outlines key installation and usage aspects, as well as code examples to help developers integrate the library into their projects.

The project is actively maintained and updated, ensuring that its methods and algorithms are up-to-date. Whisper.cpp offers flexible configuration options, allowing users to tailor it to their needs. Information on compatibility with various operating systems and hardware requirements is included to ensure a smooth integration process.

For a deeper understanding of Whisper.cpp's capabilities, the documentation also provides a description of the library's architecture and operating principles, which will be useful for both novice and experienced developers. By following the recommendations in the documentation, users will be able to make the most of Whisper.cpp in their projects.

Whisper.cpp is a local version of the Whisper model from OpenAI, optimized and rewritten in C++. This version is characterized by high performance, making it suitable for use on low-end PCs. Whisper.cpp can efficiently convert speech to text, translate from various languages into English, and perform real-time transcription. For ease of use, the program features a graphical interface, whispercppGUI, compatible with Windows and Linux operating systems.

Screenshot: Topping1 / GitHub

Ollama — a simple model manager with a catalog and API

System requirements for working with machine learning models include a minimum of 8 GB of RAM for small models and 16 GB for models of 7B and above. Additionally, at least 10 GB of free disk space is required to install the application and store models. Disk space may vary depending on the number of installed models.

To install Ollama, download the installer from the official website or load the model through the terminal by running the ollama run gemma3 command. This process will allow you to quickly and easily get started with Ollama and utilize its capabilities.

Ollama documentation provides all the necessary resources for effective use of the platform. On the website, you will find detailed instructions, guides, and usage examples to help you get the most out of Ollama's functionality. We strive to make the learning process simple and accessible for all users. Explore the sections on installation, configuration, and basic functions to quickly master the platform. Detailed articles and FAQs will help answer your questions and resolve potential issues. Ollama Docs is your reliable source of information for successful use of the platform.

Ollama initially functioned exclusively as a CLI tool and a local server for running models. It now offers a fully functional graphical interface that allows users to interact via chat, select models from the built-in catalog, and work with them as conveniently as in LM Studio. This update significantly simplifies the process of working with models and improves user experience.

We loaded the built-in Gemma 3:4b model and asked the neural network to explain its capabilities. The model reported that she is able to work with a variety of content formats in Russian, including the creation of poetry.

Screenshot: Ollama / Skillbox Media

Text Generation Web UI — browser interface for local Chatbots

System requirements for working with neural networks include a minimum of 8 GB of RAM. The amount of video memory depends on the specific model: neural networks with 7 billion parameters will require from 7 to 10 GB of video memory. It is also important to take into account that you need to allocate 10 GB of disk space for installing the program, and an additional 20 to 50 GB will be required for storing models.

To install the program, download the portable version corresponding to your operating system. Unzip the archive and run one of the files: use start_windows.bat for Windows or start_linux.sh / start_macos.sh for Linux and macOS. Then go to http://127.0.0.1:7860 to access the application interface.

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Text Generation Web UI is a powerful web interface designed for local deployment of language models. It supports major model formats and includes a built-in loader from the Hugging Face catalog. This solution serves as a full-fledged alternative to the OpenAI API, making it especially convenient for developers looking to integrate artificial intelligence into their projects without having to send data to external servers. Using Text Generation Web UI allows for greater security and control over data, making it an ideal choice for developing and testing language-based applications.

Image: oobabooga / GitHub

ComfyUI — a modular designer for creating images based on
System requirements for optimal program operation include a minimum of 8 GB of RAM and 4 GB of video memory for the graphics card. The program requires approximately 5-6 GB of free disk space. Please also note that additional space must be reserved for each downloaded model.
To install the program, download the portable version corresponding to your operating system. Unzip the downloaded archive. Run the run_nvidia_gpu.bat file to work with the graphics processor or the run_cpu.bat file to use without a graphics card.
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ComfyUI is a web application that runs in a browser and supports not only Stable Diffusion but also a variety of other models for generating images, audio, video, and 3D objects. The intuitive, block-based interface allows users to connect various elements like in a construction kit. This architecture enables the creation of complex data processing chains without writing code, making ComfyUI accessible to a wide audience, including those without technical experience.
You can create a flowchart that includes loading a photo through an input node, connecting a style transformation node, and adding a scaling node at the end. This will allow you to obtain an image processed in the chosen artistic style and at the desired resolution. For practice, upload your portrait, apply the style of Van Gogh's paintings, and increase the resolution to 4K. This way, you can evaluate how styling and increasing the resolution affect the image quality.

Image: ComfyUI

DeepFaceLab is a popular open-source neural network for deepfakes

System requirements for installing the program include Windows 10 or 11 operating systems and at least 16 GB of RAM are required. The GPU version requires an NVIDIA graphics card that supports CUDA and has at least 4 GB of video memory. It is also important to have at least 10 GB of free disk space to install the program and the required models. CPU versions of the program are compatible with most personal computers; however, their performance will be significantly lower than the GPU version.

The developer warns of a high risk of overheating on laptop components when using version 2.0. Therefore, it is recommended to avoid using it on this category of devices. This may lead to reduced performance and hardware damage. To ensure stable operation and longevity of your laptop, it is better to use more suitable versions of the software.

To install, download the ready-made build from the developer's cloud storage. Unzip the archive and run the required file. By following these simple steps, you can install the program quickly and easily.

DeepFaceLab 2.0: User Guide

DeepFaceLab 2.0 is a powerful tool for creating deep fakes using deep learning technologies. In this guide, you will find all the necessary steps for successfully using the program, from installation to configuration and video processing.

To get started with DeepFaceLab 2.0, you will need to download and install the latest version of the program from the official website. After installation, it is important to familiarize yourself with the system requirements to ensure stable operation of the application.

The next step is data preparation. You need to collect and prepare facial images that will be used to create fakes. It is important that the images are high-quality and diverse, which will achieve the best results.

Once the data is prepared, you can proceed to model training. DeepFaceLab 2.0 provides a variety of settings that allow you to tailor the training process to your needs. At this stage, it is important to monitor the quality of the training to avoid data loss and deterioration of the results.

Once the model is trained, you can begin the process of replacing faces in the video. DeepFaceLab 2.0 offers an intuitive interface for this task. Keep an eye on the settings and output parameters to get a high-quality result.

Finally, after completing all the steps, you can save and export your video. Make sure to check the final result for artifacts and other imperfections.

With DeepFaceLab 2.0, you can create high-quality deepfakes that can be used in a variety of projects. Read the documentation and experiment with the settings to achieve the best results.

DeepFaceLab is one of the most popular tools for generating deepfakes. This software product has a unique architecture, where all operations are performed through the execution of numbered batch files located in the root directory. For example, the file «1. 'extract images from video.bat' is responsible for extracting frames from the video, and '5. train.bat' initiates the model training process. Thanks to its flexibility and powerful capabilities, DeepFaceLab allows users to create high-quality deepfakes, making it a popular choice among developers and researchers in image processing.

Creating a high-quality deepfake can take anywhere from a few hours to a few days, depending on the speed of your computer and the complexity of the source material. Before you begin, ensure you have a stable power supply and disable sleep mode. This will ensure a continuous process and increase the chances of a successful outcome.

Frame: Deepfakery / YouTube

Advantages and Limitations of Local Neural Networks

Local neural networks offer many advantages, including the following. Firstly, they provide high data processing speed, which is especially important for applications that require immediate responses. Secondly, such neural networks help maintain data confidentiality, since the information does not leave the local infrastructure. This is especially important in conditions of strict personal data protection requirements. Thirdly, local neural networks can be adapted to the specific needs of a business, which increases their efficiency. In addition, the use of local neural networks reduces data transmission costs and reduces the load on internet channels. These advantages make local neural networks an ideal solution for many companies looking to optimize their processes and improve data security.

Autonomy. Models run on your computer without connecting to remote servers or cloud services.
Confidentiality. All calculations occur locally, and data is not transferred anywhere. Therefore, you can, for example, process company documents without fear of leakage.
Unlimited requests. You can use the model as much as you need, with no limits on the number of requests. And of course, everything is free.
Flexibility and customization. You can adapt models to your tasks, adjust launch parameters, and experiment with versions. For example, when translating technical texts, you can increase the temperature parameter to generate more creative versions, or decrease it for the most accurate translation. Such fine-tuning is not available in all cloud services.

The wide variety of available local neural networks deserves special mention. In this article, we looked at only ten popular models, but by turning to platforms such as Hugging Face and other similar resources, you can find thousands of models suitable for almost any task.

Screenshot: Hugging Face / Skillbox Media

When analyzing the shortcomings of this software, you should First of all, pay attention to its high system requirements. For effective operation, a modern, powerful computer is essential. For example, running the full version of Stable Diffusion XL requires a graphics card with at least 12 GB of memory, which is equivalent to an NVIDIA RTX 3080 or newer. Therefore, users should assess their devices' compliance with these requirements in advance to avoid performance issues and ensure stable operation of the program.

Another major issue is that most models require manual tuning to achieve acceptable results. If problems arise after tuning, you will have to find solutions yourself. For example, if the LLaMA 3 model starts producing errors when loading context, you will have to analyze logs, investigate similar cases on GitHub Issues, or ask for help on forums in the hopes of finding useful tips. This requires time and effort, which can be a significant barrier for users.

Modern cloud neural networks from companies such as DeepSeek, Microsoft Copilot, and Claude offer users the option of free or limited access to their services. These platforms simplify the process of interacting with artificial intelligence, eliminating the need for complex setup, constant updates, or concerns about compatibility with your hardware. The development team handles all technical aspects, and users only need to create a clear and well-thought-out prompt. Using cloud-based neural networks allows you to focus on tasks and achieve quick results without unnecessary hassle.

Analyzing the advantages and disadvantages of local models, it can be concluded that they are primarily of interest to enthusiasts, developers, and organizations with high security requirements. These solutions are also suitable for those who frequently work offline and need neural assistance on-hand. For other users, using local models may be difficult, slow, or inconvenient.

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Copilot from Microsoft: Free Access to a Neural Network in Russia

Copilot from Microsoft is a powerful artificial intelligence-based tool that significantly simplifies the process of working with code and text. Users in Russia can take advantage of this neural network's capabilities completely free of charge. To get started, you need to register on the Microsoft platform and gain access to Copilot.

An important aspect is that Copilot integrates with various IDEs and text editors, allowing developers to receive hints and recommendations in real time. This makes the programming process more efficient and less labor-intensive.

There are several steps to using Copilot for free in Russia. First, you need to create an account on the Microsoft website, then go to settings and activate Copilot. The neural network offers users not only coding assistance but also text generation, which can be useful for creating content, writing articles, and even automating routine tasks.

Microsoft's Copilot is an excellent solution for developers, students, and anyone looking to increase their productivity. Using the neural network, users can reduce the time spent on tasks and focus on the more creative aspects of their work.

Example of running a local neural network on a laptop

If you're just starting out with local neural networks, we recommend installing LM Studio or a similar application with a graphical interface. They have a similar interface to ChatGPT, which will ease the onboarding process. Using these platforms will allow you to quickly become familiar with the functionality and capabilities of neural networks.

After installation, launch LM Studio and select the search icon in the left sidebar to view a list of available neural networks. In this section, you can filter models by popularity, number of downloads, and other parameters. We recommend enabling the "Only include Staff Picks that can fit on my device" option to display only those models that are compatible with your device. This will allow you to quickly and easily find suitable neural networks for work.

Screenshot: LM Studio / Skillbox Media

After selecting the model, click the «Download» button and start working after the download is complete. We installed Gemma 2 2B, which is recommended by Google for use on devices with limited resources. This model is optimized for efficient operation, providing high performance even on low-end devices.