Ollama vs LM Studio: Which Should You Use for Local AI?

📚 More on this topic: Run Your First Local LLM · LM Studio Tips & Tricks · Ollama Troubleshooting · llama.cpp vs Ollama vs vLLM · Planning Tool

You’ve decided to run AI locally. You’ve got a capable GPU. Now you need software to actually run the models—and the two names that keep coming up are Ollama and LM Studio.

Both are free. Both run the same underlying models. Both work on Windows, Mac, and Linux. So which one should you use?

The short answer: it depends on how you like to work. The longer answer—and the one that’ll save you time—is that you’ll probably end up using both for different things. Here’s how they compare and when to use each.

What Each Tool Does

Ollama — The Command-Line Powerhouse

Ollama is an open-source tool that brings Docker-style simplicity to running LLMs. You install it, run a single command like ollama run llama3.1, and you’re chatting with a model. No GUI, no configuration wizards—just a terminal and a running model.

Under the hood, Ollama uses llama.cpp for inference and maintains its own curated library of 100+ pre-configured models. It exposes a REST API that’s compatible with OpenAI’s format, making it trivial to plug into existing applications.

Key characteristics:

• Fully open-source
• Command-line interface
• API-first design
• Curated model library with one-command downloads
• Lightweight (~500MB-1GB RAM when idle with model loaded)
• Background service that starts automatically

LM Studio — The Visual Approach

LM Studio is a desktop application with a polished GUI that makes browsing, downloading, and chatting with models feel like using a native app. You search for models, click download, and start chatting—all without touching a terminal.

It connects directly to Hugging Face and lets you browse thousands of models with filters for size, quantization, and compatibility. LM Studio also runs a local server with an OpenAI-compatible API, so it’s not just a chat interface.

Key characteristics:

• Closed-source (free for personal and commercial use as of July 2025)
• Full graphical interface
• Integrated model browser with Hugging Face access
• Built-in chat interface with conversation management
• More resource-intensive (~2-3GB RAM when idle)
• Multi-GPU controls and advanced memory management

Side-by-Side Overview

Installation and Setup

Installing Ollama

Ollama installation is a single command on Mac and Linux:

curl -fsSL https://ollama.com/install.sh | sh

On Windows, download the installer from ollama.com. It now runs natively (no WSL required) on Windows 10 22H2 or newer.

After installation, Ollama runs as a background service and starts automatically on login. To run your first model:

ollama run llama3.1:8b

That’s it. Ollama downloads the model and drops you into a chat session. The whole process takes under 5 minutes on a decent connection.

Installing LM Studio

LM Studio is a standard desktop app:

1. Download from lmstudio.ai
2. Run the installer
3. Launch the app
4. Browse models, click download, click chat

The interface guides you through everything. On first launch, it detects your hardware and suggests compatible models. You can be chatting with your first model in under 10 minutes, most of which is download time.

Winner: Setup Experience

LM Studio wins for absolute beginners. The visual interface eliminates any intimidation factor. You see what you’re doing at every step.

Ollama wins for speed and simplicity. If you’re comfortable with a terminal, one command beats clicking through an installer. And having it run as a background service means it’s always ready.

Model Support and Availability

Ollama’s Model Library

Ollama maintains a curated library at ollama.com/library with 100+ models, all pre-configured and tested. Popular options include:

• Llama 3.1 (8B, 70B, 405B) — Meta’s flagship
• DeepSeek-R1 — State-of-the-art reasoning
• Qwen 2.5 — Strong multilingual performance
• Mistral/Mixtral — Fast and capable
• Phi-3 — Microsoft’s efficient small model
• Gemma 2 — Google’s open models
• CodeLlama / DeepSeek Coder — For programming tasks

Running a model is straightforward:

ollama run deepseek-r1:8b
ollama run qwen2.5:14b
ollama run codellama:34b

The trade-off: Ollama’s library is curated, not comprehensive. If a model isn’t in their library, you need to create a Modelfile to run it—doable but requires extra steps.

LM Studio’s Model Discovery

LM Studio connects directly to Hugging Face and lets you browse thousands of models with a search interface. You can filter by:

• Parameter size
• Quantization format (Q4, Q5, Q8, etc.)
• Model family
• Hardware compatibility

This gives you access to virtually every open-weight model available, including niche fine-tunes and the latest releases that might not be in Ollama’s library yet.

The trade-off: More choice means more decisions. Beginners sometimes download models that don’t work well on their hardware.

Winner: Model Access

LM Studio wins for breadth—you can run almost anything from Hugging Face.

Ollama wins for simplicity—the curated library means everything “just works” without worrying about compatibility.

Popular Models: Availability Check

For mainstream models, both tools have you covered. The difference only matters for obscure or brand-new releases.

Performance Comparison

Inference Speed

Here’s the thing: both Ollama and LM Studio use llama.cpp under the hood. They’re running the same inference engine. Performance differences come down to:

• Overhead: Ollama has less GUI overhead; LM Studio’s interface costs a few percent
• Optimizations: LM Studio has partnered with NVIDIA for CUDA-specific optimizations
• Default settings: Each tool makes different default choices for memory allocation

Real-world benchmarks show:

The differences are small. You won’t notice 5% in normal use. Both tools can hit 60+ tokens/second on an 8B model with a modern GPU.

Memory Management

Both tools automatically manage VRAM and will spill to system RAM when a model exceeds GPU memory. Performance drops significantly when this happens (5-20x slower), so fitting in VRAM matters more than which tool you use.

Ollama’s approach:

• Aggressive VRAM utilization
• Models stay loaded by default (configurable via OLLAMA_KEEP_ALIVE)
• Known memory leak issue in versions before 0.7.0 (fixed in newer releases)

LM Studio’s approach:

• Visual memory usage indicators
• Preview memory requirements before loading
• More conservative defaults, easier to understand what’s happening

Winner: Raw Performance

Tie with caveats. On NVIDIA GPUs, LM Studio has a slight edge thanks to CUDA optimizations. On Apple Silicon, Ollama tends to be faster. The differences are small enough that other factors (interface preference, workflow needs) should drive your choice.

GUI vs CLI: The Real Tradeoffs

When a GUI Makes Life Easier

LM Studio’s interface shines for:

• Model discovery: Browsing and comparing models visually beats reading documentation
• Experimentation: Changing parameters, comparing outputs, saving conversations
• Learning: Seeing what’s happening helps you understand how local LLMs work
• Prompt testing: Chat interface with history makes iteration fast
• Demos and teaching: Showing others what local AI can do

If you’re exploring, experimenting, or showing someone what’s possible, LM Studio’s GUI removes friction.

When the CLI Wins

Ollama’s command-line approach excels at:

• Automation: Scripts, cron jobs, CI/CD pipelines
• Integration: Embedding in other applications via the API
• Repeatability: Same command, same result, every time
• Resource efficiency: No GUI overhead, runs headless on servers
• Remote access: SSH into a machine and run models without forwarding a display

If you’re building something that uses LLMs—a chatbot, a coding assistant, an automation pipeline—Ollama’s CLI and API-first design fits naturally.

The Hybrid Option

Both tools offer OpenAI-compatible API servers:

Ollama: Always running at http://localhost:11434

LM Studio: Start the server from the GUI, runs at http://localhost:1234

This means you can use LM Studio’s GUI for exploration and testing, then point your application at Ollama for production. Or use LM Studio’s server mode when you want its specific model or settings.

Resource Usage

RAM Consumption

Ollama is lighter because it’s just a service with no GUI. LM Studio’s Electron-based interface adds baseline memory usage.

VRAM Utilization

Both tools use VRAM similarly—the model weights and KV cache dominate. Key differences:

Ollama:

• Keeps models loaded by default (faster subsequent responses)
• Can run multiple models simultaneously (VRAM permitting)
• Use ollama ps to see what’s loaded, ollama stop to unload

LM Studio:

• Unloads models when you switch or close
• Visual VRAM usage display
• Can estimate requirements before loading with --estimate-only

Background Resource Usage

Ollama runs as a background service and starts on login by default. When no model is loaded, it uses minimal resources (~100MB RAM). Models unload after 5 minutes of inactivity by default.

LM Studio only runs when you launch it. No background processes, no auto-start. This is better if you want full control over when resources are used.

Resource Comparison Table

Best Use Cases

Use Ollama If…

• You’re a developer building applications that need LLM capabilities
• You prefer the terminal and want minimal overhead
• You’re automating workflows with scripts or scheduled tasks
• You need a local API that’s always available
• You’re running on a server or headless machine
• You want open-source and community transparency
• You’re integrating with tools like Continue, Open Interpreter, or custom apps

Example workflow: You’re building a coding assistant that uses a local LLM. Ollama runs in the background, your IDE plugin hits its API, and you never think about it.

Use LM Studio If…

• You’re new to local LLMs and want a gentle introduction
• You prefer visual interfaces over command lines
• You’re experimenting with different models to find what works
• You want to test prompts with a proper chat interface
• You’re teaching or demoing local AI to others
• You have an NVIDIA GPU and want CUDA-specific optimizations
• You need multi-GPU controls with visual configuration

Example workflow: You’re exploring which model works best for your use case. You download several options, chat with each, compare responses, and find your favorite—all without writing a single command.

Can You Use Both?

Yes, absolutely. Many users run both tools, and they don’t conflict.

Ollama runs on port 11434 by default. LM Studio’s server runs on port 1234. They can run simultaneously, serving different models or the same model for different purposes.

A Common Setup

1. Install Ollama for always-on API access and automation
2. Install LM Studio for model exploration and visual testing
3. Use LM Studio to discover new models and experiment
4. Point production tools at Ollama for stability and scriptability

This gives you the best of both worlds: LM Studio’s discoverability and Ollama’s reliability.

They Share Models (Sort Of)

Both tools download models to their own directories and use their own naming conventions. They don’t share model files. If you want the same model in both, you’ll download it twice.

This isn’t as wasteful as it sounds—you typically use different models for different purposes, and storage is cheap compared to the convenience of having both tools configured independently.

The Verdict

Recommendation by User Type

The Bottom Line

LM Studio is the better starting point for most beginners. The visual interface removes barriers, model discovery is intuitive, and you can be productive immediately without learning any commands.

Ollama is the better choice for developers and automation. Its API-first design, open-source nature, and lightweight footprint make it ideal for integration into larger workflows.

The real answer: Install both. Use LM Studio when you want to explore and experiment. Use Ollama when you want to build and automate. They solve different problems and coexist peacefully.

Local AI isn’t a single-tool journey. Start with whatever feels comfortable, and add the other when you need what it offers.

Related Guides

• Run Your First Local LLM in 15 Minutes
• GPU Buying Guide for Local AI
• How Much VRAM Do You Need for Local LLMs?
• Local AI Planning Tool — VRAM Calculator