Complete Guide to Local AI [2026] — How to Use Ollama, LM Studio & Jan

Running AI on your own PC without sending data to the cloud — "local AI" — is gaining significant attention. With benefits including privacy protection, cost savings, and offline use, here is your guide to getting started with local AI.

Benefits of Local AI

• Privacy: Your data never leaves your machine, making it safe to handle confidential information
• Cost: No API fees. Once set up, you can use it for free indefinitely
• Offline use: Works without an internet connection
• Customization: Full freedom to fine-tune models and configure custom settings

Major Local AI Tools

Ollama

A command-line-focused local AI runtime. Offers the simplest setup — you can run AI models with just a few commands.

```bash # After installation, run a model ollama run llama3.1 ollama run gemma2 ollama run codellama ```

• Supported OS: Windows / Mac / Linux
• Key features: Simple, lightweight, API-compatible
• Best for: Developers comfortable with CLI

LM Studio

A GUI-based local AI runtime. Model search, download, and execution all happen through the GUI, making it beginner-friendly.

• Supported OS: Windows / Mac / Linux
• Key features: Intuitive UI, model search, chat interface
• Best for: Users who prefer GUI, AI beginners

Jan

An open-source local AI client. With a ChatGPT-like UI, you can switch between local models and cloud APIs (OpenAI, Claude, etc.) in the same interface.

• Supported OS: Windows / Mac / Linux
• Key features: ChatGPT-style UI, cloud API integration, extensions
• Best for: Users who want to use both local and cloud AI

Hardware Requirements

Running local AI requires certain hardware specs. Requirements vary by model size.

Small Models (7B–8B parameters)

• RAM: 8GB or more
• GPU VRAM: 6GB or more (CPU-only works but slower)
• Recommended models: Llama 3.1 8B, Gemma 2 9B, Phi-3

Medium Models (13B–34B parameters)

• RAM: 16GB or more
• GPU VRAM: 12GB or more
• Recommended models: Llama 3.1 70B (quantized), Mixtral 8x7B

Large Models (70B+)

• RAM: 32GB or more
• GPU VRAM: 24GB or more (RTX 4090, etc.)
• Recommended models: Llama 3.1 70B, Qwen 2.5 72B

Recommended Quantized Models

If your VRAM is limited, quantized models let you reduce memory usage while maintaining reasonable accuracy.

• Q4_K_M: Balanced — good trade-off between accuracy and speed
• Q5_K_M: Slightly higher quality — use if you have headroom
• Q8_0: High quality but high memory consumption

Local AI Use Cases

• Internal document summarization & analysis: Process confidential data without sending it to the cloud
• Coding assistance: Use CodeLlama or DeepSeek Coder for development support
• Learning & experimentation: An environment for understanding how AI works
• Personal knowledge base: Build a RAG-powered personal Q&A system

Conclusion

Local AI is a practical option for leveraging AI while protecting your privacy. With Ollama, setup takes just minutes. Start with a small model — even PCs without a GPU can run 8B-class models comfortably.