Ollama and Jan AI are the two most popular tools for running AI models locally on your own hardware. Both download and run open-weight models with zero cloud dependency. Both are free. But they target different users.

Ollama is CLI-first — built for developers who want a local inference server they can integrate into tools and scripts. Jan is GUI-first — built for anyone who wants a ChatGPT-like interface running entirely on their machine.

Quick comparison

Where Ollama wins

Developer integration

Ollama is the backbone of local AI development. It powers:

• Aider via --model ollama/modelname
• Continue (VS Code extension)
• OpenCode
• Open WebUI (web chat interface)
• Any tool that supports OpenAI-compatible endpoints

Jan’s API works too, but far fewer tools support it natively.

Server-mode (always running)

Ollama runs as a daemon — start once, it stays running in the background. Any tool can call it anytime via http://localhost:11434. Jan requires the desktop app to be open.

Docker deployment

Official Docker image for containerized deployments, server installations, and CI/CD pipelines. Jan has no Docker support.

CLI speed

Pull a model and start chatting in two commands:

ollama pull qwen3.6:27b
ollama run qwen3.6:27b

Model switching

Models load and unload in seconds. Switch between a 7B model (quick questions) and a 27B model (complex coding) instantly. Ollama manages memory automatically.

Lightweight

Small daemon, minimal RAM when idle. Jan is an Electron app with higher baseline resource consumption.

Where Jan AI wins

Built-in chat interface

Jan provides a beautiful ChatGPT-like interface out of the box. No need to pair with Open WebUI or other frontends. For people who just want to chat with a local model, Jan is ready immediately.

Non-technical friendly

Download the app, click a model, start chatting. No terminal, no commands, no API knowledge. Perfect for non-developers who want local AI for writing, research, or conversation.

Conversation management

Save, organize, and search past conversations in the GUI. Ollama’s raw API has no conversation persistence — you need a frontend for that.

Hugging Face integration

Browse and download models directly from Hugging Face within the app. Ollama uses its own model library (which is large but separate from HF).

Visual model management

See model sizes, RAM requirements, and download progress visually. Ollama requires ollama list and memory monitoring via terminal.

Performance comparison

Both use llama.cpp under the hood. Performance is essentially identical for the same model at the same quantization:

Model	Ollama	Jan AI	Difference
Qwen 3.6 27B (Q4)	~25-35 t/s	~25-35 t/s	Negligible
Llama 4 Scout (Q4)	~10-15 t/s	~10-15 t/s	Negligible
7B model (Q4)	~60-80 t/s	~60-80 t/s	Negligible

The speed difference is in the interface overhead, not the inference. Jan’s Electron UI adds minor latency to the display but not to token generation.

Use case recommendations

You want to…	Best choice	Why
Integrate with coding tools (Aider, Continue)	Ollama	Native support everywhere
Chat with AI locally (no terminal)	Jan AI	Built-in GUI
Run on a server (headless)	Ollama	Daemon mode, Docker
Run on your laptop casually	Jan AI	App experience
Use as API backend for custom apps	Ollama	Better API, more stable
Show non-technical friends local AI	Jan AI	No terminal needed
Run in Docker/Kubernetes	Ollama	Official container
Manage many models efficiently	Ollama	CLI model management

Can you use both?

Yes. They use different ports (Ollama: 11434, Jan: 1337) and can run simultaneously. Some developers use Ollama as their always-on API server and Jan as a quick chat interface when they want a visual conversation.

Also consider

• LM Studio — GUI like Jan but with more advanced features (quantization control, server mode). The middle ground between Ollama and Jan.
• Open WebUI — Web-based chat interface that connects to Ollama. Gives you Jan-like UI with Ollama’s backend.
• vLLM — Production inference server. For when Ollama isn’t fast enough.

FAQ

Which has more models available?

Both have access to most popular open-weight models. Ollama’s library (ollama.com/library) is curated and easy to browse. Jan connects to Hugging Face (much larger but less curated). For popular models (Qwen, Llama, Gemma, DeepSeek), both have them.

Which uses less RAM?

Identical for model inference (same backend). Ollama’s daemon uses less idle RAM than Jan’s Electron app. Difference is ~200-500MB — negligible on modern machines.

Can I switch from Jan to Ollama later?

Yes. Models are in GGUF format for both. You can redownload via Ollama or point Ollama at existing GGUF files. No lock-in.

Which is better for coding?

Ollama — because it integrates with Aider, Continue, OpenCode, and other coding tools natively. Jan is primarily a chat tool, not a coding assistant.

Is one faster than the other?

No. Both use llama.cpp. Same model + same quantization + same hardware = same speed. The difference is in the UI and integration, not inference performance.

Which for RTX Spark?

Ollama. It will be the default local AI tool on RTX Spark, with NVIDIA-optimized llama.cpp builds for 2× throughput on Blackwell hardware.