The XDA piece on ditching Claude for Obsidian and a local LLM captured the shift that has been building since open-weight models became genuinely useful: you can do most of your daily AI work on your own desktop now, without sending a token to anyone’s API. The hardware bar is low enough that a 16 GB MacBook Air or a current mid-range gaming PC handles 7B and 8B parameter models with the kind of latency you would expect from a hosted service.
We tested the 8 best apps for running LLMs locally on desktop. The list spans drag-and-drop GUIs for people who have never used a terminal, command-line runtimes that drop into existing scripts, and self-hosted web interfaces that turn an old desktop into a household AI server. Each pick was judged on model coverage, hardware acceleration, the quality of the chat interface, and how painful the first-run setup is.
What to look for in a local LLM app
Pick a local LLM app that:
- Ships with a working model the first time you open it. Apps that require you to assemble a quantization, a tokenizer, and a chat template before the first message waste a Saturday.
- Uses GPU acceleration where the hardware allows. CUDA on Nvidia, Metal on Apple Silicon, ROCm or Vulkan on AMD — the speed difference is large.
- Supports an OpenAI-compatible API endpoint. The whole ecosystem of editors, agents, and tools speaks the OpenAI Chat Completions format; an app that exposes one becomes infrastructure.
- Manages model files cleanly. A folder full of 8 GB GGUF files with no UI to remove them is a recipe for a full disk.
- Stays current with the model release calendar. Llama, Qwen, Mistral, and Gemma all shipped major updates in 2025; the apps that lag a quarter behind become uninteresting fast.
Quick comparison
| App | Best for | Platforms | Free plan | Starting price |
|---|---|---|---|---|
| LM Studio | Polished GUI for first-time users | Windows, macOS, Linux | Yes, fully | Free for personal |
| Ollama | Command-line workflow and scripting | Windows, macOS, Linux | Yes, fully | Free |
| Jan | Open-source LM Studio alternative | Windows, macOS, Linux | Yes, fully | Free |
| GPT4All | Privacy-first local chat with documents | Windows, macOS, Linux | Yes, fully | Free |
| Open WebUI | Self-hosted ChatGPT-style web UI | Linux, Docker (any OS) | Yes, fully | Free |
| Msty | Offline chat with side-by-side model comparison | Windows, macOS, Linux | Yes, limited | Around $50 one-time |
| Llamafile | Single-file portable model runner | Windows, macOS, Linux | Yes, fully | Free |
| LocalAI | Self-hosted OpenAI-compatible API server | Linux, Docker | Yes, fully | Free |
The 8 best local LLM apps for desktop
1. LM Studio — best polished GUI for first-time users
LM Studio is the easiest entry point into running models locally. The download is a regular desktop app, the model browser shows curated GGUF builds with size and recommended hardware next to each one, and the chat interface is good enough to use as a daily driver. Discovery, download, configuration, and chat all live in one window, and the OpenAI-compatible server runs with a single toggle for tools that need an API endpoint.
Where it falls short: The app is closed-source for the GUI, which is a real disqualifier for some users. Power-user features like multi-model agentic workflows are not the focus.
Platforms: Windows 10/11, macOS (Apple Silicon and Intel), Linux x86_64.
Bottom line: Install this first, get your hardware tested with a 7B model, then graduate to other tools as needed.
2. Ollama — best command-line workflow
Ollama is the local-LLM tool that has shaped how the rest of the ecosystem talks to models. ollama run llama3.1 downloads the weights and drops you into a prompt; ollama serve exposes the OpenAI-compatible API on port 11434, which every desktop editor, agent framework, and chat front-end now speaks. The model library is large, updates land within days of new releases, and the CLI integrates cleanly into shell scripts.
Where it falls short: There is no first-party GUI. Ollama assumes you are comfortable in a terminal and pairs best with a separate front-end like Open WebUI or Msty.
Platforms: Windows, macOS, Linux. Docker image available.
Bottom line: The default backend for everything else in this list. Install it even if you also install LM Studio.
3. Jan — best open-source LM Studio alternative
Jan is what LM Studio would look like if the team had open-sourced it from day one. The interface mirrors LM Studio’s three-panel layout, the model library covers the same major families, and the API endpoint speaks the same OpenAI dialect. Where Jan pulls ahead is in agentic features — multi-model assistants, MCP server integration, and a plugin architecture that lets the community add capabilities without forking.
Where it falls short: Smaller model catalogue than LM Studio’s curated browser, and the polish gap on first-run is visible. Stability under heavy use has improved through 2025 but is still behind LM Studio.
Platforms: Windows, macOS, Linux. Open-source under the Apache 2.0 license.
Bottom line: Pick Jan when “open-source” is the deciding factor and you do not need LM Studio’s catalogue polish.
4. GPT4All — best privacy-first local chat with documents
GPT4All from Nomic AI focuses on document-grounded chat without any data leaving the machine. The LocalDocs feature indexes a folder of PDFs, markdown, or plain text and lets the model answer questions against that corpus — entirely offline, with no embeddings sent to a cloud service. The default model selection leans toward smaller quantizations that run well on CPU-only laptops.
Where it falls short: The chat interface is the basic version of the genre; power features like branching conversations and multi-turn tool use are missing. Larger 30B+ models work but are slower than LM Studio on the same hardware.
Platforms: Windows 10/11, macOS, Linux. Open-source under the MIT license.
Bottom line: The right pick for “chat with my files” on a laptop where the files must never leave the disk.
5. Open WebUI — best self-hosted ChatGPT-style interface
Open WebUI turns a local Ollama or LocalAI install into a polished web app that feels like ChatGPT — multi-user accounts, conversation history, RAG against uploaded documents, model switcher, and prompt library. The intended deployment is Docker on a home server or workstation, then everyone in the household opens it from a phone or laptop browser.
Where it falls short: It is a front-end, not a model runtime — you still need Ollama or LocalAI behind it. Initial Docker setup takes 30 minutes for first-timers.
Platforms: Anywhere Docker runs — Linux, Windows with WSL, macOS, Synology, Unraid, Proxmox.
Bottom line: The right pick when you want a household-shared local AI that looks and feels like ChatGPT in a browser.
6. Msty — best offline chat with model comparison
Msty is built around a feature most local LLM apps miss: side-by-side responses from two or more models to the same prompt. The split view makes it obvious when a smaller model is good enough and when the larger one earns its disk space. Msty also handles long conversations well, with branching threads and a knowledge stack for document grounding.
Where it falls short: The desktop app is closed-source. The free tier covers most everyday use but the lifetime license is sold for advanced features.
Platforms: Windows, macOS, Linux.
Bottom line: Pick Msty when you want to do real evaluation across models without juggling three windows.
7. Llamafile — best portable single-file model runner
Llamafile from Mozilla packages a model and a runtime into one executable file that runs on Windows, macOS, and Linux without installation. Double-click the .llamafile, a chat interface opens in your browser at localhost, and you have a working model. It is the simplest possible deployment for “send a working local LLM to someone who does not know what a GGUF is.”
Where it falls short: Each model is its own multi-gigabyte executable, which is wasteful if you want a library. No first-class model browser — you find files on Hugging Face and download them manually.
Platforms: Windows, macOS, Linux, FreeBSD. One file, no install.
Bottom line: The right format for getting a non-technical user up and running with a local model in under five minutes.
8. LocalAI — best self-hosted OpenAI-compatible API server
LocalAI is the headless backend for serious self-hosted setups. It exposes the full OpenAI API surface — chat completions, embeddings, audio transcription, image generation — backed by local models, with no GPU required for the smaller ones. Drop it into Docker Compose alongside Open WebUI, point your existing OpenAI client code at the localhost endpoint, and the rest of your stack works unchanged.
Where it falls short: Configuration is YAML-first and assumes container familiarity. No GUI at all — pair with Open WebUI for chat or use it purely as infrastructure.
Platforms: Linux, Docker. Runs on macOS and Windows through Docker.
Bottom line: The right pick when you are wiring local models into existing applications that already speak the OpenAI API.
How to pick the right one
If you have never run a model locally before, install LM Studio, download a Qwen or Llama 8B quantization at Q4_K_M, and chat. The whole sequence takes 15 minutes including the model download. When you outgrow it, install Ollama so the rest of your tools have an API to talk to.
If “open-source” is non-negotiable, go straight to Jan for the GUI and Ollama for the backend. If you want to chat with documents that must never leave your machine, install GPT4All and feed it your folder. If you want a shared household AI in a browser, run Open WebUI on top of Ollama on a home server.
If you do evaluation work across models, install Msty for the side-by-side view. If you want the simplest possible “give this to a friend” delivery, point them at a Llamafile. If you are building something that talks to the OpenAI API and want a local backend, deploy LocalAI in Docker.
FAQ
What hardware do I need to run an LLM locally?
A 7B or 8B parameter model at 4-bit quantization runs comfortably on 8 GB of RAM and any GPU from the last five years, or on Apple Silicon Macs from M1 onward. For 13B models, 16 GB of RAM is the practical minimum. 70B class models need 48 GB of unified memory on a Mac or two 24 GB GPUs on a PC.
Are local LLMs as good as ChatGPT or Claude?
Not yet for the most demanding work, but the gap closed sharply in 2025. Open-weight 8B and 14B models now match the GPT-3.5 era for general chat, summarization, and code assistance. The frontier models from Anthropic, OpenAI, and Google remain ahead on long-context reasoning and tool use.
Is it safe to run local LLMs?
Yes, in the sense that no data leaves your machine. The risk surface is the model file itself — download from Hugging Face directly or through a reputable front-end like LM Studio, Ollama, or Jan. Verify checksums when a provider publishes them. Random GGUFs from forums get the same treatment as any other unsigned executable.
Can a local LLM connect to the internet?
The model itself does not have network access. You can give it tools that browse the web through an agent framework like Open WebUI’s web search, MCP servers, or your own scripting — but that is a deliberate choice you make. Out of the box, every app in this list runs fully offline.
What is the difference between Ollama and LM Studio?
LM Studio is a polished GUI that includes model discovery, chat, and an optional API server. Ollama is a CLI and server with no built-in chat interface. Most users install both — Ollama as the backend that other tools talk to, LM Studio when they want a chat window without leaving the desktop.
