Written by Jakub Rusinowski · Last updated July 10, 2026
Last Updated: May 2026 — Four tools dominate local LLM inference. Each serves a different user. This guide breaks down exactly which one fits your situation, with real setup examples and performance n
Last Updated: May 2026 — Four tools dominate local LLM inference. Each serves a different user. This guide breaks down exactly which one fits your situation, with real setup examples and performance notes.
→ Developer / CLI power user: Use Ollama → Non-technical user wanting a GUI: Use LM Studio → Maximum privacy / offline-only: Use Jan → Absolute beginner, one-click setup: Use GPT4All → VS Code / IDE integration: Use Ollama + Continue.dev → Local AI server for a team: Use Ollama + Open WebUI
| Feature | Ollama | LM Studio | Jan | GPT4All |
|---|---|---|---|---|
| Interface | CLI + REST API | Full GUI | Full GUI | Full GUI |
| Platform | Win / Mac / Linux | Win / Mac / Linux | Win / Mac / Linux | Win / Mac / Linux |
| OpenAI-Compatible API | Yes | Yes | Yes | No |
| Auto GPU Detection | Yes | Yes | Yes | Yes |
| Model Manager (GUI) | No (CLI only) | Yes | Yes | Yes |
| HuggingFace Models | Via GGUF URL | Direct browse | Direct browse | Limited |
| Built-in Chat UI | No | Yes | Yes | Yes |
| Offline / Air-Gapped | Yes | Yes | Yes (design goal) | Yes |
| Custom System Prompts | Via Modelfile | Yes | Yes | Yes |
| Multi-Model Chat | No | Yes (side-by-side) | No | No |
| Vision / Image Input | Yes (vision models) | Yes | Partial | No |
| Open Source | MIT | No (free) | AGPL-3.0 | MIT |
| IDE Plugins | Yes (Continue, Cline) | Yes | No | No |
| Active Development | Very Active | Very Active | Active | Moderate |
| Best VRAM Management | Auto | Manual sliders | Auto | Auto |
Website: ollama.com | License: MIT | Install size: ~50 MB
Ollama has become the de facto standard for running local LLMs programmatically. It packages models with their configuration, serves an OpenAI-compatible REST API on port 11434, and integrates with virtually every local AI tool.
ollama pull llama3.1 — done# Install (Linux/macOS)
curl -fsSL https://ollama.com/install.sh | sh
# Windows: download installer from ollama.com/download
# Run a model interactively
ollama run llama3.1
# Pull without running
ollama pull gemma3:27b
# List downloaded models
ollama list
# Check running models and VRAM usage
ollama psfrom openai import OpenAI
client = OpenAI(
base_url="http://localhost:11434/v1",
api_key="ollama" # required by the client, not used
)
response = client.chat.completions.create(
model="llama3.1",
messages=[{"role": "user", "content": "Explain RAG in one paragraph"}]
)
print(response.choices[0].message.content)# Create a Modelfile
cat > Modelfile << 'EOF'
FROM llama3.1:8b
SYSTEM "You are a senior Python developer. Be concise and use type hints."
PARAMETER temperature 0.2
PARAMETER num_ctx 16384
EOF
# Build and run
ollama create python-assistant -f Modelfile
ollama run python-assistantWebsite: lmstudio.ai | License: Free, proprietary | Install size: ~200 MB
LM Studio is the most polished local AI application. If you want a ChatGPT-style experience without touching a terminal, this is your tool. It downloads, manages, and runs models through a clean interface.
1. Download from lmstudio.ai and install 2. Open the Discover tab → search for "llama 3.1" → click Download 3. Switch to the Chat tab → select your model → start chatting 4. Enable the Local Server tab to expose the API at http://localhost:1234/v1
LM Studio shows a slider: "GPU Offload Layers." Set it to maximum for your VRAM. If the model doesn't fit fully, partial GPU offload still helps — some layers on GPU is much faster than all-CPU.
Website: jan.ai | License: AGPL-3.0 | Install size: ~150 MB
Jan's entire design philosophy is offline by default, open source always. Unlike LM Studio, every line of Jan's code is publicly auditable. Unlike Ollama, it ships with a built-in GUI. It's the choice for users who won't compromise on privacy or openness.
http://localhost:1337/v11. Download from jan.ai 2. Open Jan → Hub tab → browse and download models 3. Start chatting in the Thread tab 4. Enable the Local API Server in settings for external app access
Website: gpt4all.io | License: MIT | Install size: ~200 MB
GPT4All prioritizes getting you chatting with an AI in under 5 minutes, no terminal required, no configuration needed. It's built for the absolute beginner who just wants to try local AI.
Winner: Ollama The OpenAI-compatible API means your existing code works with a one-line change. Integrate with LangChain, LlamaIndex, or any OpenAI SDK in minutes. Add Open WebUI for a management interface.
Winner: LM Studio Best chat UI, easiest model discovery, no setup friction. For non-developers who want to chat locally, nothing is more polished.
Winner: Jan Fully auditable code, no telemetry, offline by design. When you need to prove to clients or regulators that no data leaves the premises, open-source software you can audit is the only defensible choice.
Winner: Ollama + Continue.dev Install Ollama, then add the Continue extension to VS Code. You get AI code completion and chat inline in your editor, powered by any Ollama model.
# Setup for VS Code integration
ollama pull qwen2.5-coder:7b
# Install Continue extension from VS Code marketplace
# Configure Continue to use ollama at http://localhost:11434Winner: Ollama + Open WebUI Run Ollama on a server, deploy Open WebUI with user accounts and SSO. Everyone on the team gets a private ChatGPT-style interface backed by your local hardware.
# On your server
OLLAMA_HOST=0.0.0.0:11434 ollama serve
# Deploy Open WebUI with multi-user support
docker run -d -p 3000:8080 \
-e OLLAMA_BASE_URL=http://your-server:11434 \
-e WEBUI_AUTH=true \
-v open-webui:/app/backend/data \
ghcr.io/open-webui/open-webui:mainAll four tools use llama.cpp under the hood (for GGUF models), so raw inference speed is essentially identical given the same model and quantization. The differences are in:
Can I use multiple tools at the same time? Yes, but they can't share the same GPU memory at once. Running Ollama and LM Studio simultaneously will cause VRAM conflicts. Use one at a time for inference.
Which tool supports the most models? LM Studio (full HuggingFace browse) and Ollama (GGUF URL import + registry) tie for the most model options. GPT4All has the smallest curated selection.
Does LM Studio send my data anywhere? LM Studio has a privacy policy and may collect anonymized usage telemetry. The actual model inference is local. If you want zero-telemetry guarantees, use Jan or Ollama (both open source with auditable network behavior).
Is Ollama's API actually OpenAI-compatible? Yes — it implements the /v1/chat/completions, /v1/completions, and /v1/models endpoints. Most code using the OpenAI Python or JavaScript SDK works with a base_url change. A few advanced features (function calling schemas, fine-tuning endpoints) may differ.
Which tool is best for vision models? Ollama and LM Studio both support vision models (Llama 3.2 Vision, Gemma 3). Open WebUI makes it easiest with drag-and-drop image uploads into chat.