How to Run AI Locally: Ollama vs LM Studio

Running an AI model on your own hardware has gone from a weekend project for ML engineers to a practical workflow for everyday developers. Two tools dominate this space: Ollama and LM Studio. Both let you download and run open-source models locally, but they're designed for different audiences and workflows. Here's a clear breakdown of which one you should use.

Ollama: Best for Developers and CLI Power Users

Ollama is a lightweight command-line tool that makes managing and running local LLMs feel as simple as using Docker. It wraps the llama.cpp backend (which handles quantisation and efficient CPU/GPU inference) in a clean API and CLI interface.

Getting Started with Ollama

1. Download from ollama.ai and run the installer (available for macOS, Linux, and Windows)
2. Open your terminal and run: ollama pull llama4:scout
3. Start chatting immediately: ollama run llama4:scout
4. Or call it via API: http://localhost:11434/api/generate

The REST API is fully compatible with the OpenAI SDK. You can point the OpenAI Python client at http://localhost:11434/v1 and your existing code will work with zero modifications — a significant advantage for developers already using OpenAI's API.

Ollama Pros

• Zero-config setup — works out of the box
• OpenAI-compatible REST API for seamless integration
• Lightweight — no GUI overhead
• Model management via simple CLI commands (pull, list, rm)
• Active model library with 100+ pre-configured models

Ollama Cons

• No GUI — not suitable for non-technical users
• Less control over quantisation format (handles it automatically)
• Limited advanced inference configuration

LM Studio: Best for Non-Technical Users and Model Exploration

LM Studio is a full desktop application with a graphical interface that makes running local models accessible to anyone — no command line required. It supports downloading models directly from Hugging Face, has a built-in chat interface, and gives you granular control over inference parameters (temperature, top-p, context length, etc.).

LM Studio Pros

• Full graphical user interface — no terminal needed
• Direct Hugging Face model browser built in
• Fine-grained control over quantisation (choose between Q4, Q5, Q8 formats)
• Built-in chat UI for testing models
• Also exposes an OpenAI-compatible local server

LM Studio Cons

• Heavier application (uses more system resources)
• Slower to get started vs Ollama's three-command setup
• Not ideal for scripted/automated workflows

Head-to-Head: Which Should You Use?

Use Case	Recommended Tool
Building an app with local AI	Ollama (API compatibility)
Non-technical user, just chatting	LM Studio (GUI)
Exploring different models	LM Studio (Hugging Face browser)
Production scripting / automation	Ollama (CLI + API)
Control over quantisation format	LM Studio
Running on a headless Linux server	Ollama

Hardware Requirements

Both tools use llama.cpp under the hood, so hardware requirements are the same. A rough guide:

• 8GB RAM (CPU only): 3B–7B parameter models (Phi-4 Mini, Mistral 7B Q4)
• 16GB RAM + 8GB VRAM: 13B–14B models (Llama 3.2, Qwen2.5-14B)
• 24GB VRAM: Full 30B–34B models (Mistral Small, Qwen3-32B)
• 2× 24GB VRAM or A100: 70B models (Llama 4 Scout, DeepSeek-V3)