Ollama vs LM Studio vs Jan
Three of the most popular tools for running large language models locally on your own hardware. We compare their installation experience, supported models, performance, and use cases — so you can pick the right one without trial and error.
Ollama
CLI-first, lightning-fast, developer-friendly
LM Studio
Beautiful GUI, Hugging Face search, beginner-friendly
Jan
Open-source desktop app, privacy-first, cross-platform
Side-by-Side Feature Comparison
| Feature | Ollama | LM Studio | Jan |
|---|---|---|---|
| Free to use | |||
| Open source | |||
| Works 100% offline | |||
| GUI desktop app | |||
| CLI interface | |||
| OpenAI-compatible API | |||
| GPU acceleration (CUDA) | |||
| Apple Metal (M-series) | |||
| AMD ROCm support | |||
| Hugging Face model search | |||
| Built-in model library | |||
| Model quantization control | |||
| Multi-model concurrency | |||
| Plugin / extension system | |||
| Built-in chat UI | |||
| Conversation history | |||
| Windows | |||
| macOS | |||
| Linux | |||
| GitHub stars | 162k | N/A | 40k |
| Minimum RAM | 8 GB | 8 GB | 8 GB |
| Recommended RAM | 16 GB | 16 GB | 16 GB |
Hardware Requirements
| Config | Ollama | LM Studio | Jan |
|---|---|---|---|
| Minimum RAM | 8 GB | 8 GB | 8 GB |
| Recommended RAM | 16–32 GB | 16 GB | 16 GB |
| Minimum VRAM (GPU mode) | 4 GB | 4 GB | 4 GB |
| Recommended VRAM | 8–24 GB | 8–16 GB | 8 GB |
| CPU-only capable | ✓ | ✓ | ✓ |
| Disk space (app) | ~100 MB | ~500 MB | ~300 MB |
| Model storage (7B) | ~4 GB | ~4 GB | ~4 GB |
All three tools support quantized GGUF models (Q4, Q5, Q8) which dramatically reduce VRAM usage. A 7B Q4 model fits in 4 GB VRAM; a 13B Q4 model needs ~8 GB.
Deep Dive: Each Tool Explained
Ollama
Ollama (2023) became the de facto standard for running LLMs locally, amassing 162k+ GitHub stars in under two years. It wraps llama.cpp with a clean REST API and a Docker-inspired CLI where pulling a model is as simple as ollama run llama3.3. It auto-detects your GPU, handles quantization, and exposes port 11434 with an OpenAI-compatible API so any tool that uses OpenAI just works with a URL swap.
Ollama's strength is its ecosystem: Open WebUI, Cursor, Continue, Aider, and dozens of other tools support it natively. Its model library now covers 100+ models (Llama 3.3, Mistral, Gemma, Phi, DeepSeek, Qwen, and more) all with one-command download. Ollama also uniquely supports running multiple models concurrently and loads on AMD ROCm.
Pros
- ✓ Fastest startup time (~1s model load)
- ✓ Full open source (MIT license)
- ✓ OpenAI API drop-in replacement
- ✓ AMD ROCm + CUDA + Metal support
- ✓ Huge ecosystem of compatible frontends
- ✓ Multi-model concurrency
- ✓ Excellent documentation
Cons
- ✗ No built-in GUI (terminal only)
- ✗ Model library smaller than Hugging Face
- ✗ Less friendly for non-technical users
- ✗ Limited fine-tuning support
LM Studio
LM Studio targets non-technical users with a polished Electron desktop app. The built-in search browses Hugging Face directly — you type a model name, see all available GGUF quantizations with size/speed trade-off info, and download with one click. The integrated chat UI supports personas, prompt templates, and context window sizing from a clean settings panel.
The local server mode exposes an OpenAI-compatible endpoint, enabling integration with Cursor, VS Code extensions, and other tools without any configuration. LM Studio's biggest trade-off is being proprietary (though free) and currently lacking AMD GPU support.
Pros
- ✓ Most polished GUI of the three
- ✓ Hugging Face model discovery
- ✓ No command line needed
- ✓ Drag-and-drop model import
- ✓ Prompt preset library
- ✓ Excellent for beginners
Cons
- ✗ Proprietary / closed source
- ✗ No AMD GPU support
- ✗ Heavier on system resources
- ✗ No scripting or automation API
- ✗ Single model at a time
Jan
Jan (by Jan HQ) is the open-source answer to LM Studio. It's a cross-platform desktop app with a clean UI, conversation management, and an extension system. Jan stores everything locally — no cloud sync, no telemetry, zero data leaves your machine. Its hub curates recommended models with clear performance profiles for different hardware tiers.
Jan also exposes an OpenAI-compatible API server and supports custom inference engines (llama.cpp, TensorRT-LLM). The extension system lets power users add integrations without touching core code. Jan's community has grown rapidly, with active Discord and GitHub discussions.
Pros
- ✓ Fully open source (AGPL)
- ✓ Beautiful GUI + conversation history
- ✓ Plugin/extension system
- ✓ Strong privacy focus
- ✓ OpenAI-compatible API
- ✓ Active development
Cons
- ✗ Smaller model hub vs LM Studio
- ✗ Occasional stability issues
- ✗ Smaller community than Ollama
- ✗ Limited AMD support
Which Should You Choose?
No terminal needed. Browse and download models from a familiar GUI. Perfect if you want to explore local AI without any setup friction.
OpenAI-compatible API, scripting support, multi-model concurrency, and a huge ecosystem. Integrates with every major dev tool.
100% open source with no telemetry. Extension system for customization. A GUI-friendly choice for users who prioritize auditability.
Our Recommendation
For most users in 2026, Ollama wins as the overall standard — its ecosystem, speed, and API compatibility are unmatched. LM Studio is the runner-up for anyone who wants a GUI without touching the terminal. Jan is the specialist pick for privacy-first, open-source advocates.
Frequently Asked Questions
Which is easiest to install: Ollama, LM Studio, or Jan?
LM Studio has the friendliest GUI installer — download and double-click. Jan also offers a polished desktop app. Ollama is a single CLI binary for advanced users who prefer the terminal.
Can Ollama, LM Studio, and Jan all run without a GPU?
Yes — all three run on CPU using GGUF-quantized models. Performance is much better with a GPU (NVIDIA CUDA or Apple Metal), but a modern CPU can run 7B models at usable speeds.
Which supports the most models?
Ollama's library has 100+ curated models. LM Studio searches Hugging Face directly, giving access to thousands of GGUF files. Jan focuses on quality-checked models from its hub.
Does any of them offer an OpenAI-compatible API?
All three expose an OpenAI-compatible REST API, making them drop-in replacements for OpenAI in local development.
Which is best for privacy?
All three run 100% offline with zero data leaving your machine. Ollama and Jan are fully open source. LM Studio's core is proprietary but processes everything locally.