Best Of
10 Best LLM Tools To Run Models Locally (August 2026)
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Running large language models locally is no longer only for researchers with unusual hardware. Developers, privacy-conscious teams, hobbyists, and small businesses can now download capable open models, run them on a laptop or workstation, connect them to documents, expose local APIs, and build private AI workflows without sending every prompt to a hosted model provider.
The best local LLM tools solve different parts of that workflow. Some make model discovery and chat simple. Some provide a self-hosted interface for teams. Some focus on retrieval-augmented generation, document chat, and agents. Others are lower-level runtimes for developers who care about inference speed, hardware control, quantization, API compatibility, and deployment flexibility.
The right tool depends on what you are trying to do. A nontechnical user may want a desktop app that can chat with local documents. A developer may want an OpenAI-compatible local server. A company may need a self-hosted interface with users, permissions, audit logs, and model routing. An enthusiast may want maximum control over model formats, sampling, backends, and extensions. This list focuses on tools that are useful for running, managing, testing, and building with local models in practical settings.
Best Tools for Running LLMs Locally Compared
| Tool | Best For | Key Strengths |
|---|---|---|
| Ollama | Best overall local model runtime for developers and everyday AI apps | Model library, CLI, desktop app, local server, OpenAI compatibility, tools, embeddings, multimodal model support |
| LM Studio | User-friendly local model discovery, testing, and API development | Model browser, local chat, offline operation, REST API, OpenAI and Anthropic compatibility, SDKs, CLI, Bionic agent |
| Open WebUI | Self-hosted AI interface for teams and mixed model environments | Ollama and OpenAI-compatible model support, RAG, tools, Python functions, web search, voice, vision, users, permissions, SSO |
| AnythingLLM | Private document chat, RAG, local agents, and team workspaces | Desktop and Docker apps, local models, document workspaces, RAG, reranking, agents, MCP, agent flows, scheduled jobs |
| Jan | Open-source desktop alternative to hosted AI chat tools | Local and cloud models, offline use, agents, projects, assistants, file uploads, local API server, MCP, model hub, local data storage |
| Msty Studio | Private AI workspace for local and cloud model workflows | Local and online models, knowledge stacks, agents, personas, skills, prompts, workflows, split chat, encrypted secrets |
| LocalAI | Self-hosted OpenAI-compatible AI infrastructure across many modalities | OpenAI-compatible API, modular backends, text, vision, speech, images, video, embeddings, reranking, agents, CPU-to-cluster deployment |
| llama.cpp | High-performance local inference and low-level hardware control | GGUF models, quantization, CPU and GPU acceleration, CLI, local server, OpenAI-compatible endpoints, model conversion, broad hardware support |
| GPT4All | Private local model chat and document search on everyday computers | Desktop app, CPU-friendly inference, LocalDocs, local API server, Python SDK, GGUF models, local embeddings, Windows, macOS, Linux |
| TextGen | Advanced local-model experimentation, configuration, and extensions | Desktop app, GGUF support, multiple backends, text and vision, tool calling, web search, document chat, APIs, training, extensions |
How to Choose a Local LLM Tool
Start with the interface you actually need. If the goal is private chat on one machine, a desktop app such as LM Studio, Jan, GPT4All, AnythingLLM, Msty Studio, or TextGen may be enough. If the goal is to build an application, Ollama, LocalAI, llama.cpp, LM Studio, or TextGen can expose local endpoints that developers can connect to. If the goal is a shared internal AI workspace, Open WebUI and AnythingLLM become more relevant because they can support users, documents, retrieval, tools, and team workflows.
Hardware matters more than marketing language. Smaller quantized models can run on ordinary machines, but larger models need enough memory, GPU support, and patience. The same model can feel fast or unusable depending on quantization, context length, backend, CPU, GPU, and whether other apps are competing for resources. A good local setup begins with a realistic model size and a clear understanding of what the hardware can sustain.
Security and privacy also need nuance. Running locally can keep prompts, documents, embeddings, and outputs on your own machine or infrastructure, but only if the tool is configured that way. Many tools can also connect to cloud models or online features. Review model downloads, telemetry settings, remote access, API exposure, document storage, browser access, authentication, and whether the tool is intended for one user or a shared deployment.
10 Best LLM Tools to Run Models Locally
1. Ollama
Ollama is the easiest default recommendation for many people who want to run local models from the command line, a desktop app, or an application. It handles model downloads, local serving, model management, and a simple developer workflow around popular open models. Its OpenAI-compatible API support also makes it easier to connect existing tools and applications to a local model server.
The reason Ollama earns the top spot is that it works well as a foundation. Beginners can use it to pull and run models quickly, while developers can build around its local endpoints, embeddings, tool support, and multimodal model capabilities. It is also widely supported by other tools in the local AI ecosystem, including interfaces and RAG platforms that use Ollama as the underlying runtime.
Pros and Cons
- Simple model download, runtime, CLI, desktop, and local-server workflow
- OpenAI-compatible API support makes it easy to connect tools and apps
- Strong ecosystem support across local AI interfaces and developer projects
- Good fit for both beginners and developers building local AI workflows
- Advanced users may want more direct backend and sampling control
- Model performance still depends heavily on hardware and quantization choices
- Team interfaces usually require pairing Ollama with another front end
2. LM Studio
LM Studio is one of the most polished desktop tools for discovering, downloading, testing, and serving local models. It gives users a friendly interface for exploring model files, chatting locally, running offline after models are downloaded, and exposing local APIs for apps and scripts.
The developer side is a major strength. LM Studio supports local REST APIs, OpenAI-compatible flows, Anthropic-compatible flows, SDKs, a CLI, and workflows for connecting local models to notebooks, backend services, and custom tools. Its newer Bionic agent direction also broadens the product from model testing into local work and coding assistance. LM Studio is best for users who want a friendly desktop experience without giving up serious developer pathways.
Pros and Cons
- Excellent desktop experience for finding, testing, and running local models
- Local API, SDKs, CLI, and compatibility layers are useful for developers
- Can operate offline after model files are available
- Good balance between approachable UI and application-development support
- Desktop-first design may not fit every self-hosted team deployment
- Large models still require capable hardware
- Users should understand when they are using local models versus remote options
3. Open WebUI
Open WebUI is a self-hosted AI interface for people and teams that want one place to work with local and cloud models. It pairs especially well with Ollama, but it can also connect to OpenAI-compatible providers and other model sources. Users can chat, attach files, search the web, use tools, run Python functions, and work across different models from one browser-based environment.
Open WebUI is strongest when a single-user desktop chat app is not enough. It supports user management, permissions, model routing, retrieval workflows, voice, vision, image generation, and administrative features that matter in shared deployments. For organizations that want local models available through a familiar interface, Open WebUI is one of the most practical front ends.
Pros and Cons
- Strong self-hosted interface for local and cloud model access
- Excellent companion to Ollama and OpenAI-compatible endpoints
- Supports files, RAG, tools, Python functions, web search, voice, and vision
- Team-oriented features make it useful beyond one-person desktop use
- Requires hosting and administration compared with a desktop-only app
- Security depends on deployment, authentication, and network exposure
- More powerful than needed for someone who only wants a simple local chat window
4. AnythingLLM
AnythingLLM is built around private productivity with local models, documents, workspaces, and agents. It gives users desktop and Docker options, lets them work with local documents, supports retrieval-augmented generation, and can run AI workflows without requiring a cloud model key for many local setups.
The platform is especially useful when the goal is not merely chatting with a model, but grounding that chat in documents and repeatable workflows. Teams can organize knowledge into workspaces, use RAG and reranking, build agents, connect MCP tools, create agent flows, and schedule jobs. AnythingLLM is a strong choice for people who want private document chat and local agents without assembling every piece manually.
Pros and Cons
- Strong document chat, RAG, workspace, and agent workflow features
- Desktop and Docker options cover individual and team use cases
- Can work with local models and private documents in a practical interface
- MCP, agent flows, and scheduled jobs make it useful beyond simple chat
- Quality depends on document preparation, retrieval settings, and model choice
- Teams should plan workspace permissions and data organization carefully
- May be more structure than needed for users who only want model testing
5. Jan
Jan is an open-source desktop alternative to hosted AI chat products. It can run open models locally, connect to cloud models when the user chooses, store data locally, expose a local API server, and support projects, assistants, agents, file uploads, a model hub, and MCP workflows.
Jan is strongest for users who want a familiar AI chat experience with more control over where models and data live. It is approachable enough for everyday use, but technical enough to connect with local APIs and emerging agent tooling. For privacy-first users who want a polished open-source desktop app, Jan is one of the most compelling options.
Pros and Cons
- Open-source desktop app with a familiar local AI chat experience
- Can run local models and connect to cloud models when needed
- Supports projects, assistants, file uploads, local APIs, agents, and MCP
- Good fit for privacy-conscious users who want a polished interface
- Desktop-first workflow is less suited to centrally managed team deployments
- Local model quality and speed depend on user hardware
- Users should distinguish clearly between local and cloud model modes
6. Msty Studio
Msty Studio is a private AI workspace for people who want to work with local and online models side by side. It brings together chats, knowledge stacks, personas, prompt workflows, agents, skills, media tools, split conversations, encrypted secrets, and workspace organization in a studio-like environment.
The product is most useful for people who use AI throughout the day and want more structure than a single chat thread. Knowledge stacks can ground conversations in selected material, personas and skills can standardize recurring work, and split chats make it easier to compare models or approaches. Msty Studio fits users who want a private workspace for local AI work rather than only a lightweight model runner.
Pros and Cons
- Private workspace for local and online models in one interface
- Knowledge stacks, personas, skills, prompts, and agents support repeatable work
- Split chat and model comparison are useful for serious AI users
- Good fit for people who want a daily AI workspace, not only a runtime
- More workspace-oriented than infrastructure-oriented
- Users should verify which tasks run locally and which use online models
- May be excessive for someone who only wants command-line local inference
7. LocalAI
LocalAI is a self-hosted AI engine for teams and developers who want OpenAI-compatible APIs running on their own hardware. It supports a modular backend architecture across text, vision, speech, sound, images, video, embeddings, reranking, and agent-style workloads, with deployment options ranging from local machines to containers and clusters.
The practical appeal is infrastructure control. Instead of treating local AI as a desktop app, LocalAI lets developers expose familiar APIs behind their own model stack. It can run different backends as needed, support varied hardware, and provide a drop-in style interface for applications already designed around hosted AI APIs. LocalAI is best for builders who want self-hosted AI services, not just a chat UI.
Pros and Cons
- OpenAI-compatible self-hosted API for local and private AI infrastructure
- Supports many modalities and modular backends
- Useful for developers migrating app workflows away from hosted endpoints
- Can scale from local hardware to more serious infrastructure deployments
- Requires more technical setup than desktop local-model apps
- Deployment quality depends on backend, model, hardware, and operations choices
- Not intended as the simplest beginner chat interface
8. llama.cpp
llama.cpp is one of the foundational projects behind modern local LLM inference. Its main purpose is to make LLM inference possible with minimal setup and strong performance across a wide range of hardware. It is closely associated with GGUF models, quantization, CPU and GPU acceleration, command-line workflows, local serving, and low-level control over how models run.
For nontechnical users, llama.cpp may feel closer to infrastructure than an app. For developers and enthusiasts, that is the point. It provides the engine-level control that many other local tools build on or depend upon. If you care about model formats, quantization, hardware efficiency, inference behavior, grammar constraints, and squeezing useful performance from local machines, llama.cpp remains essential.
Pros and Cons
- Foundational local inference project with broad hardware support
- Excellent for GGUF models, quantization, CLI workflows, and performance tuning
- Useful local server and compatibility options for developers
- Gives technical users deep control over inference behavior
- Less approachable than desktop apps for beginners
- Requires comfort with model files, command-line options, and hardware tradeoffs
- Users wanting polished chat, documents, or team features need a front end
9. GPT4All
GPT4All is a local AI desktop application from Nomic for running language models privately on everyday computers. It is designed to be approachable: download the application, choose a model, chat locally, and use features such as LocalDocs to bring private files into model conversations without relying on a hosted chat service.
Its biggest advantage is accessibility. GPT4All is a good fit for users who want private local chat and document search without learning a full developer stack. LocalDocs, local embeddings, desktop support across major operating systems, and a local API server make it useful for both personal productivity and lightweight experimentation. It is not the deepest infrastructure platform on this list, but it remains one of the friendliest paths into local AI.
Pros and Cons
- Approachable desktop app for private local model chat
- LocalDocs helps users work with their own documents on-device
- Runs on common desktop operating systems and supports local APIs
- Good fit for users who want local AI without complex setup
- Less flexible than low-level runtimes or self-hosted infrastructure tools
- Model performance depends on ordinary desktop hardware limits
- Advanced team deployment and governance needs require other tools
10. TextGen
TextGen, from the oobabooga project, is an open-source desktop app for local LLMs that grew out of the well-known text-generation-webui ecosystem. It supports local text and vision workflows, tool calling, web search, document chat, APIs, GGUF models, multiple backends, training options, and extensions for users who want deep control.
TextGen is best for advanced enthusiasts and experimenters who want a powerful local interface with more configuration than the typical desktop chat app. It can be approachable enough to launch from a desktop build, but its real value appears when users want to test backends, tune settings, experiment with extensions, or work closer to the model stack. It is the tinkerer’s option on this list.
Pros and Cons
- Powerful open-source local LLM app with deep configuration options
- Supports GGUF models, multiple backends, APIs, tool calling, documents, and extensions
- Useful for experimentation, model testing, and advanced local workflows
- Desktop builds make the project more accessible than older setup paths
- Can be more complex than mainstream desktop local AI tools
- Advanced features require comfort with model and backend configuration
- Best suited to enthusiasts rather than nontechnical teams seeking governance
Final Thoughts
The best local LLM tool depends on whether you need a runtime, a desktop chat app, a document workspace, or self-hosted infrastructure. Ollama is the best overall starting point because it is simple, widely supported, and developer-friendly. LM Studio, Jan, GPT4All, Msty Studio, and TextGen are strongest for desktop local AI workflows. Open WebUI and AnythingLLM are better when documents, users, tools, RAG, and shared workspaces matter. LocalAI and llama.cpp are the deeper infrastructure choices for developers who want local APIs, deployment control, and hardware-level inference flexibility.
