XDA ran two stories this month that put the local-LLM question front and center: a home lab piece about a 284-billion-parameter DeepSeek V4 Flash running on-device, and a follow-up on dropping ChatGPT once a local model was reachable from anywhere over Tailscale. Both come back to the same tools. If you want the AI without the subscription, without the data leaving your machine, and without a rate limit, the best apps for running local LLMs on desktop are surprisingly close to plug-and-play in 2026.
We tested eight apps across a Windows 11 desktop with a 16 GB GPU, an M3 MacBook Pro, and a Fedora workstation. Each is judged on how quickly you can pull a working model, how well it handles a mixed workload (chat, code, long docs), and whether it plays nicely with other tools.
What to look for in a local LLM app
- Model catalog. Does the app fetch popular open weights (Llama, Qwen, DeepSeek, Mistral, Gemma) without you writing YAML.
- Quantization presets. Runs 4-bit, 5-bit, and 8-bit quants without wrestling GGUF flags.
- GPU offload. Uses your NVIDIA, AMD, or Apple Silicon GPU without a fight.
- OpenAI-compatible API. So tools that already speak the OpenAI API (VS Code Continue, Zed, Open WebUI, LangChain) can plug in.
- Multi-user or single-user. Personal machine vs. a small team on a home server.
- RAG and file chat. Drop a PDF or folder and get answers grounded in that content.
Quick comparison
| App | Best for | Platforms | Free plan | Paid | Rating |
|---|---|---|---|---|---|
| Ollama | Simplest local model runner | Windows, macOS, Linux | Fully free | None | 4.9 |
| LM Studio | Model discovery + GUI chat | Windows, macOS, Linux | Fully free | None | 4.8 |
| Jan | Open-source ChatGPT-style client | Windows, macOS, Linux | Fully free | None | 4.6 |
| GPT4All | Local + optional cloud in one app | Windows, macOS, Linux | Fully free | None | 4.5 |
| Msty | Split-chat comparison across models | Windows, macOS, Linux | Fully free | Aurum $99 lifetime | 4.7 |
| AnythingLLM | Turn local models into an app | Windows, macOS, Linux | Fully free | Cloud from $50/mo | 4.6 |
| text-generation-webui | Power-user experimentation | Windows, macOS, Linux | Fully free | None | 4.4 |
| Open WebUI | ChatGPT-style UI on your Ollama | Windows, macOS, Linux | Fully free | None | 4.8 |
The apps
1. Ollama — Best for the simplest local model runner
Ollama is the piece of software that made local LLMs feel routine. Install it, run ollama run llama3.2, and you’re chatting. It fetches, quantizes, and serves models with an OpenAI-compatible API on port 11434.
Where it falls short: No native chat GUI, just a CLI plus API. You’ll pair it with Open WebUI or Msty for a chat interface.
Pricing:
- Free: Fully free, open source.
- Paid: None.
Platforms: Windows, macOS, Linux.
Download: ollama.com
Bottom line: The runtime everyone else builds on. Start here even if you also install a fancier chat client.
2. LM Studio — Best for model discovery + GUI chat
LM Studio is the all-in-one desktop app: model browser (Hugging Face integrated), quantization selector, chat UI, and a local API server. It’s the fastest way to try three different DeepSeek quants side by side.
Where it falls short: Not open source, and Linux support is behind Windows/macOS. Some enterprise use cases need a paid arrangement.
Pricing:
- Free: Fully free for personal use.
- Paid: Contact sales for work use.
Platforms: Windows, macOS, Linux.
Download: lmstudio.ai
Bottom line: The best all-in-one GUI for a personal machine. It’s what most people who “just want to try local LLMs” should install.
3. Jan — Best for an open-source ChatGPT-style client
Jan is the open-source, offline-first ChatGPT clone. It runs on Cortex, its own inference engine, and it can also talk to Ollama, LM Studio, and OpenAI-compatible endpoints. Assistants, threads, and file uploads are built in.
Where it falls short: Some models still need a manual download step. Startup on Windows can be slow on older machines.
Pricing:
- Free: Fully free, open source.
- Paid: None.
Platforms: Windows, macOS, Linux.
Download: jan.ai
Bottom line: The pick if you want a ChatGPT-shaped app that’s fully local and fully open source.
4. GPT4All — Best for one app that talks to local and cloud
GPT4All ships a friendly desktop chat that runs local GGUF models and can also route to OpenAI or Groq when you want. LocalDocs lets you point at a folder and get grounded answers.
Where it falls short: Model catalog is a step behind LM Studio’s. GPU offload works but takes more knob-turning on Windows.
Pricing:
- Free: Fully free, open source.
- Paid: None.
Platforms: Windows, macOS, Linux.
Download: gpt4all.io
Bottom line: A good pick for someone who wants a single client that can do local now and cloud tomorrow.
5. Msty — Best for comparing models side by side
Msty puts two or three model responses next to each other in a split view. Useful when you’re deciding whether Qwen 2.5 or DeepSeek V3 is better for your prompt style. It talks to Ollama, LM Studio, and cloud APIs.
Where it falls short: Some advanced features are behind the paid Aurum tier. Not open source.
Pricing:
- Free: Fully free chat and split view.
- Paid: Aurum $99 lifetime unlocks knowledge stacks and premium features.
Platforms: Windows, macOS, Linux.
Download: msty.app
Bottom line: The best pick if you evaluate models often and want to see them argue.
6. AnythingLLM — Best for turning a local model into an app
AnythingLLM puts a full RAG pipeline behind your local model. Point it at a folder, a website, a Confluence, or a Notion, and it becomes a searchable assistant. It also exposes agents that can call tools.
Where it falls short: Setup for the multi-source pipeline takes longer than opening LM Studio. The polished cloud offering is paid.
Pricing:
- Free: Desktop is fully free.
- Paid: AnythingLLM Cloud from around $50/mo.
Platforms: Windows, macOS, Linux.
Download: anythingllm.com
Bottom line: Pick this when a chat window isn’t enough and you need a document-grounded assistant.
7. text-generation-webui — Best for power-user experimentation
The oobabooga-flavored text-generation-webui is the Gradio-based interface that supports every backend imaginable (llama.cpp, ExLlamaV2, transformers, TensorRT-LLM). LoRAs, character cards, fine-tuning hooks — it’s here.
Where it falls short: The learning curve is real. Errors read like a research repo, not a consumer app.
Pricing:
- Free: Fully free, open source.
- Paid: None.
Platforms: Windows, macOS, Linux.
Download: github.com/oobabooga/text-generation-webui
Bottom line: Pick this if you want to run every backend on every model. Not for casual users.
8. Open WebUI — Best for a ChatGPT-shaped UI over Ollama
Open WebUI runs in a Docker container and gives you a ChatGPT-style multi-user web app pointed at Ollama or any OpenAI-compatible endpoint. It supports RAG, functions, and shared conversations.
Where it falls short: It’s a web app, not a native desktop client. You bring the runtime (Ollama) yourself.
Pricing:
- Free: Fully free, open source.
- Paid: None.
Platforms: Windows, macOS, Linux (runs in Docker).
Download: openwebui.com
Bottom line: The best pick when the machine running the model isn’t the machine you sit at, or when a household wants shared chat.
How to pick the right one
If you want the simplest working setup: Ollama + Open WebUI. Ollama serves the model, Open WebUI gives you a chat. One command each.
If you want a single desktop app: LM Studio. It’s the smoothest all-in-one.
If open source matters: Jan or Ollama + Open WebUI. Both are fully open, both are polished.
If you want to compare models: Msty. Split view is the killer feature.
If you want to build something with local models: AnythingLLM or text-generation-webui. Both give you an API surface and a plugin story.
If you’re on Tailscale and want ChatGPT to come from home: run Ollama on your desktop, put Open WebUI on the same box, expose it through your mesh, and the phone finally reaches your model the same way it reaches ChatGPT.
FAQ
What is the best free local LLM app? Ollama for the runtime, LM Studio if you want a GUI. Both are free for personal use.
What model should I run first? Llama 3.2 8B for a general-purpose starter on modest hardware, Qwen 2.5 14B if you have 12 GB of VRAM or more, and DeepSeek V3 for coding tasks.
Do I need a GPU? No, but it helps. CPU-only inference works for small models. A 12 GB VRAM GPU (or 24 GB unified memory on Apple Silicon) unlocks the interesting mid-size models.
Can I use a local LLM in VS Code or Zed? Yes. Point the Continue extension at Ollama’s OpenAI-compatible endpoint, or use Zed’s inline assistant with the same endpoint.
Is it safe to use these apps offline? That’s the point. Ollama, Jan, LM Studio, and GPT4All all run without an internet connection once the model is downloaded.
