9 Best Local LLM Apps of 2026

LM Studio ranks first because it removes the most friction between a new user and a working local model. You install one app on macOS, Windows or Linux, search Hugging Face from inside it, download a model and chat. Under the hood it runs llama.cpp everywhere and Apple's MLX on Apple Silicon. The docs say the app can operate entirely offline once you have model files, and document chat (RAG) also runs offline. Developers get an OpenAI-compatible local server, a REST API, the lms command line, Python and TypeScript SDKs and a headless mode called llmster for machines without a screen. LM Studio also works as an MCP client, so local models can use tools. The current app ships the Bionic agent for documents, code and computer control, with real-time voice transcription processed on the device. Paid plans only add cloud inference: Bionic+ is $20 a month and Pro is $100. The main drawback for privacy purists is that the app is closed source.

AnythingLLM is built around the most common reason people want a local model: asking questions about their own documents. Every file you add becomes part of a workspace knowledge base, and the vendor says models, documents and chat history stay on your machine with nothing phoning home. The desktop app installs from one file on macOS (Apple Silicon and Intel), Windows (x64 and ARM) and Linux, needs no account, and can pick a model for your hardware. Its built-in engine now runs llama.cpp managed by AMD's Lemonade, and on Windows it can use Microsoft Foundry Local to reach the CPU, GPU and NPU. Beyond chat it adds a no-code agent builder, MCP support, scheduled jobs, web search and an on-device meeting assistant that transcribes and summarizes calls without a bot joining. The project is MIT licensed and can be self-hosted with Docker for free. A hosted Cloud instance costs $50 a month for Basic or $99 for Pro.

Ollama is the engine behind a large share of local AI setups, and that is why it ranks second. It installs with one command on macOS, Linux or Windows, ships an official Docker image and has Python and JavaScript libraries. Running the ollama command prompts you to start a model or connect it to agents and apps such as Claude Code, OpenClaw, OpenCode, Codex and Copilot. Open WebUI, AnythingLLM and Msty Studio can all use it as a backend. The project is MIT licensed, and Ollama reports more than 9 million installs a month. The pricing page says running models on your own hardware is always unlimited; the paid plans are for cloud models that run on Ollama's servers. Pro costs $20 a month with $60 of usage credits, and Max costs $100. Ollama says prompt and response data is never logged or trained on. On its own it is less of a chat workspace than LM Studio or Jan, so many people pair it with a separate interface.

Jan is the open source alternative to LM Studio for people who want the whole stack under a permissive license. Menlo Research describes it as an open source replacement for ChatGPT and Claude that runs entirely on your hardware, licensed under Apache 2.0 and built in public. You install it on macOS, Windows or Linux, from the website, the Microsoft Store or Flathub, and start chatting without an account. It ships Jan's own foundation models tuned for reasoning, agents and research, and you can also load other open models or connect cloud providers when a task needs a frontier model. A local API server exposes your models to other apps, and Jan supports MCP servers and integrations with Claude Code and OpenClaw. Newer pieces such as Cowork, which works on files and commands on its own, are in preview, and cross-chat memory is still listed as coming soon. Jan costs nothing, which gives it one of the best value scores here.

Open WebUI is the richest self-hosted chat interface for local models, which is why it scores highest on capabilities after LM Studio. The project describes itself as an extensible, self-hosted platform built to run entirely offline. You install it with pip or Docker, open it in a browser, and connect it to Ollama or any OpenAI-compatible server, local or cloud. The toolkit covers voice, vision, retrieval over documents, image generation and web search, and you can extend it with Python tools and functions or install items from a community library. Because it is a multi-user web app, one machine with a good GPU can serve a whole household or office, and the enterprise tier adds SSO, role-based access, audit logs and air-gapped deployment. The trade-offs are setup and licensing: it does not run models by itself, it is a browser app rather than a native one, and its license requires you to keep the Open WebUI branding.

KoboldCpp is the most capable single-file tool here. The project calls itself free and open source software for running GGUF models on your own computer, and it ships as one executable for Windows, Linux or macOS with no installation. It runs on CPU or GPU, with full or partial offloading, so it also works on modest hardware. Text generation covers chat, adventure, instruct and story modes, and the bundled KoboldAI Lite interface adds memory, world info, author's notes and imports of Tavern character cards, which is why it is popular for fiction and roleplay. It goes well beyond text: image generation and editing with Stable Diffusion, SDXL and Flux, video generation, Whisper speech recognition, several text-to-speech engines and music generation. MCP, tool calling, web search and a built-in agent cover newer workflows, and compatible endpoints let apps that expect OpenAI, Ollama or ComfyUI connect to it. The interface has many settings, which costs it points on ease of setup.

Msty Studio is the most feature-dense desktop workspace on this list. It chats with local models through Ollama, llama.cpp or MLX and with online models from one hub, and you can split chats to compare answers. Knowledge Stacks give you retrieval over your own files, Agent Mode and Crew conversations let several personas work on a task, and separate studios store reusable prompts, personas, skills and media. MCP tools are supported. CloudStack lists zero product telemetry and customer-controlled storage, but also says SOC 2 and ISO 27001 certification are not yet complete. The free plan is permanent, yet many items in the feature matrix are marked limited, and Studio Web, Shadow Personas, Turnstiles workflow automation and Forge Mode need Aurum at $149 per user a year or a $349 lifetime license. That pricing is why Msty scores lower on value than the free open source apps, even though its capabilities score is high.

TextGen, the project long known as text-generation-webui, is the toolbox for people who want control over how models run. The portable builds for Windows, Linux and macOS come with CUDA, Vulkan, ROCm or CPU-only options and run GGUF models after you unzip and double-click. A fuller install adds ExLlamaV3, Transformers and TensorRT-LLM backends, and you can switch backends and models without restarting. It exposes OpenAI and Anthropic-compatible endpoints, so other apps can use it as a drop-in local API, and models can call single-file Python tools or MCP servers. It is one of the few apps here that trains LoRAs on chat or raw text datasets, and it also attaches images, PDFs and Word files and generates images with diffusers models. For privacy, the project states it is 100% offline with zero telemetry, external resources or remote update requests. The cost is complexity: the interface and install options assume you know what a backend is.

llamafile takes a different approach from every other app here: the model and the software ship as one executable file. Mozilla.ai combines llama.cpp with Cosmopolitan Libc so the same file runs on most operating systems and CPU architectures with no installation. You download a pre-built llamafile, make it executable on macOS, Linux or BSD, or add .exe on Windows, and run it. You can also run the llamafile binary with any GGUF weights you already have, and the companion whisperfile does speech-to-text and translation the same way. That makes it the easiest way to move a model onto an offline or air-gapped computer. Version 0.10 moved to a new build system that tracks recent llama.cpp releases, and the project notes it may miss some features of the classic builds, which stay available. The main limits are a basic interface and Windows refusing executables over 4GB, which rules out larger bundled models there.