If you're comparing Ollama vs LM Studio, you're choosing between two ways to run large language models locally: a developer-first CLI tool versus a polished desktop app with a visual interface.
Both tools let you download models, run them on your own hardware, and avoid API costs. The difference is in how you interact with them—and what you're trying to build.
The core difference
Ollama is a command-line tool that works like Docker for LLMs. You pull models with a single command, run them instantly, and access them via API. It's designed for developers who want to script, integrate, and automate.
LM Studio is a desktop application with a graphical interface. You browse available models, download them with a click, and chat with them in a built-in window. It's designed for people who want to experiment without touching a terminal.
Both tools use llama.cpp under the hood—and on Mac, LM Studio also supports MLX for better Apple Silicon performance. The raw inference capabilities are similar; the difference is in the experience.
When to choose Ollama
Ollama wins when you're building something:
Developer workflows: Pull models with
ollama pull llama3, run them withollama run llama3, script everythingAPI access: Built-in REST API that's OpenAI-compatible—swap out cloud APIs for local inference with minimal code changes
Server use cases: Run Ollama as a background service, call it from your apps, expose it to your tools
Automation: Chain model calls in scripts, integrate with n8n or other workflow tools, build pipelines
Ollama's strength is that it stays out of your way. There's no GUI to navigate—just commands and an API. If you're comfortable in a terminal, you'll be productive in minutes.
# Install Ollama (macOS/Linux)
curl -fsSL https://ollama.com/install.sh | sh
# Pull and run a model
ollama pull llama3.2
ollama run llama3.2
# Use the API
curl http://localhost:11434/api/generate -d '{
"model": "llama3.2",
"prompt": "Explain quantum computing briefly."
}'
When to choose LM Studio
LM Studio wins when you want a visual experience:
Model discovery: Browse Hugging Face directly in the app, see hardware requirements, download with a click
Experimentation: Try different models and parameters without writing commands
Chat interface: Built-in conversation window—no need to set up a separate UI
Mac optimization: MLX backend delivers faster inference on Apple Silicon than llama.cpp alone
LM Studio is genuinely beginner-friendly. If you've never run a local model before, you can go from download to conversation in a few minutes without touching a terminal.
For Mac users specifically, LM Studio's MLX support often delivers better performance: benchmarks show 237 tokens/second on Gemma 3 1B with LM Studio vs 149 t/s with Ollama on the same hardware.
Performance and compatibility
Both tools support the same model formats (GGUF) and can run most popular open-source models: Llama 3, Mistral, Phi, Gemma, Qwen, and many others.
Speed: On identical hardware, LM Studio with MLX often outperforms Ollama on Apple Silicon. On Linux/Windows with NVIDIA GPUs, performance is more comparable.
Memory: LM Studio tends to be more memory-efficient for single-user scenarios. Ollama handles concurrent requests better due to request batching.
Model availability: Ollama has a curated library (~100+ models). LM Studio can load any GGUF file from Hugging Face (~30,000+ options).
The practical question
Most people don't need to choose exclusively. The tools serve different moments:
Use LM Studio when you want to quickly try a new model or have a conversation
Use Ollama when you're building something that needs programmatic access to local inference
They can coexist on the same machine. Many developers use LM Studio for exploration, then switch to Ollama when they need to integrate a model into an app or workflow.
Running local LLMs on Zo
If you're on Zo Computer, you can run Ollama as a persistent service. Zo handles the server; you get local inference you can call from scripts, agents, or other tools running on your Zo.
This gives you the best of both worlds: Ollama's API-first design running on infrastructure you don't have to manage, accessible from your automations and workflows.
Decision framework
Choose Ollama if:
You're comfortable with command-line tools
You want to call models from scripts or apps
You need a background service with API access
You're building automated workflows
Choose LM Studio if:
You prefer graphical interfaces
You want to browse and try models quickly
You're on Mac and want MLX performance
You're experimenting rather than building
For most developers, the answer is: start with whichever matches your current task. If you're exploring, LM Studio gets you there faster. If you're integrating, Ollama is the obvious choice.