Running large language models on your own machine means privacy, no monthly fees, and offline access. We tested the top tools — from developer-friendly terminals to polished desktop apps — to find the best way to run local LLMs on Mac and PC.
A few years ago, running a capable language model on your laptop sounded like science fiction. Today it's a practical reality. The shift toward local LLMs is driven by three things: privacy (your data never leaves your machine), cost (no per-token API bills), and offline access (no internet required).
But the tooling landscape is fragmented. Should you use a terminal? A desktop app? Something in between? Here's what we found after testing the top options.
Before we get to the picks, it's worth understanding why local inference matters.
Data sovereignty. When you use ChatGPT or Claude, your prompts travel to a remote server. For sensitive work — legal documents, medical notes, proprietary code — that's a non-starter. Local models keep everything on your hardware.3
Cost. Heavy API users know the pain. A single project can rack up hundreds of dollars in token fees. Local models are free to run (you pay for the hardware once).1
Offline reliability. No internet? No problem. Once the model is downloaded, it works anywhere — planes, cabins, subway tunnels.
The OpenAI-compatible API standard. Most local tools now expose an API endpoint that mirrors OpenAI's format. That means you can swap out GPT-4 for a local model by changing one line of code in your app.1
We've grouped these by who they're for. Developers will want the terminal-first approach. Everyone else should start with a GUI.
Ollama is the default. One command pulls a model. Another runs it. Within thirty seconds you've got an OpenAI-compatible API running on localhost:11434.1
It supports dozens of models — Llama 3, Mistral, DeepSeek, Phi, Gemma — and handles GPU acceleration on both Apple Silicon (Metal) and NVIDIA (CUDA) out of the box. The model library is curated, so you don't waste time hunting down broken quantized files on Hugging Face.
For developers building AI-powered apps, Ollama is the obvious starting point. It's lightweight, well-documented, and the community around it is massive.
Who it's for: Developers, tinkerers, anyone comfortable with a terminal.
If the terminal isn't your thing, LM Studio is the most user-friendly way to run local LLMs. It offers a clean desktop interface, a built-in model marketplace, and a chat window that looks and feels like ChatGPT — but everything runs locally.2
You can browse, download, and test models side-by-side without writing a single command. It also exposes an OpenAI-compatible local API, so developers can use it as a backend too.
LM Studio handles GPU acceleration automatically and includes a server mode for running models in the background. It's the best bridge between "I just want to chat with a local model" and "I need programmatic access."
Who it's for: Non-technical users, writers, researchers, and developers who prefer a GUI.
Jan takes a different approach. It's built by a community with a user-owned philosophy — the software is fully open-source, and your data stays on your device. It runs popular models like DeepSeek R1 and Llama without requiring an internet connection.3
What sets Jan apart is its hybrid interface: it works as a local chat app but also offers a remote inference mode for when you want access to cloud models. You control the switch. It's a thoughtful middle ground for people who want privacy but also occasional access to larger models they can't run locally.
Jan is still younger than Ollama and LM Studio, so the ecosystem is smaller. But the privacy-first ethos and clean design make it a strong contender.
Who it's for: Privacy-conscious users who want an open-source tool with both local and cloud options.
The real question isn't which tool is "best" — it's which workflow fits you.
| Dimension | CLI (Ollama) | GUI (LM Studio) |
|---|---|---|
| Setup time | ~30 seconds | ~2 minutes |
| Learning curve | Terminal required | Point and click |
| API access | Built-in, trivial | Built-in, simple |
| Model discovery | Command line search | Visual marketplace |
| GPU optimization | Auto (Metal/CUDA) | Auto (Metal/CUDA) |
| Best for | Developers, automation | Everyone else |
Both Ollama and LM Studio expose OpenAI-compatible APIs. Both support Apple Silicon and NVIDIA GPUs. The difference is the interface — and that's a personal choice.
Local LLMs are demanding. Here's what you'll need for a good experience:
Both Ollama and LM Studio handle GPU acceleration automatically on Apple Silicon (Metal) and NVIDIA (CUDA).1
If you're a developer, start with Ollama. It's the fastest path from zero to a working local API. If you prefer a visual interface or you're new to local LLMs, LM Studio is the best place to start. And if privacy and open-source ownership are your top priorities, Jan is worth a close look.
All three tools are free. All three support the OpenAI-compatible API standard. And all three let you run powerful models on your own hardware — no cloud required.
Disclosure: Some links on this page are affiliate links. We may earn a small commission at no extra cost to you. We only recommend tools we've tested and genuinely believe in.
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