Local LLM Complete Guide 2026: Ollama vs LM Studio vs Jan
A thorough comparison of local LLM tools including Ollama, LM Studio, and Jan. Installation, supported models, and real-world performance are all covered.
As demand grows for AI usage that keeps data fully on-device, local LLMs are drawing significant attention. This guide compares the top tools for running LLMs directly on your own machine.
Why Run an LLM Locally?
There are three major benefits to local LLMs. First, privacy protection — data never leaves your machine. Second, cost savings — no API fees. Third, the ability to work offline. These advantages are especially relevant when handling sensitive corporate data or working in environments with unreliable internet connectivity.
Tool Comparison
Ollama
The command-line-based Ollama is the de facto standard for local LLMs. Its ease of use is hard to beat — type `ollama run llama3.2` and the model downloads and starts running. Because it also functions as an API server, it's widely used as a backend to give other applications access to local LLMs. It supports all major open models including Llama 3.2, Gemma 2, Phi-3, and Qwen 2.5.
LM Studio
GUI-based LM Studio lets you run local LLMs without writing any code. Model search, download, and execution all happen through a graphical interface, and you interact with models through a ChatGPT-style chat UI. Automatic detection of quantized models and fine-grained parameter controls make it accessible to newcomers and power users alike.
Jan
Jan is an open-source AI assistant with a privacy-first philosophy. It runs as a desktop application with a ChatGPT-style UI for local LLMs. Its built-in OpenAI-compatible API server makes it straightforward to switch existing OpenAI SDK applications over to local models.
Hardware Requirements
Required specs depend on model size. As a rough guide: 7B parameter models need at least 8GB RAM, 13B models need 16GB, and 70B models need 64GB. A dedicated GPU significantly speeds up inference, but CPU-only machines (including those with Apple Silicon) can run these models too.
Recommended Models
| Model | Parameters | Notes |
|---|---|---|
| Llama 3.2 3B | 3B | Lightweight and fast, runs on 8GB RAM |
| Gemma 2 9B | 9B | Well-balanced, good multilingual support |
| Qwen 2.5 14B | 14B | Strong multilingual support and coding |
| DeepSeek R1 8B | 8B | Reasoning-focused, strong at math and logic |
| Llama 3.1 70B | 70B | GPT-4 class performance |
Tips for Getting More Out of Local LLMs
Local LLMs can also serve as the backend for tools like Claude Code and Cursor. By connecting Ollama's API server via MCP, you can get AI coding assistance while keeping all your data on-device. Combined with RAG (Retrieval-Augmented Generation), you can build a fully on-premises Q&A system over your internal documents.
Conclusion
Local LLMs have made major strides in both performance and usability through 2026. For CLI users, Ollama is the best choice. For a GUI experience, LM Studio is ideal. For privacy as the top priority, Jan is the way to go. Start with a lightweight model like Llama 3.2 3B and find the model size that runs comfortably on your hardware.