Ollama and Local LLMs: Why More People Are Running AI on Their Laptops

Ollama has surpassed 150,000 stars on GitHub. More and more people are running large models locally.

Why Go Local

1. Privacy: Sensitive data doesn't need to be uploaded to OpenAI/Google
2. Cost: API calls add up over time; local is a one-time investment
3. Offline: Works on planes or in environments without internet
4. Control: Fine-tuning and freely switching between models

The M4 Ultra Push

Apple's M4 Ultra unified memory (256GB) makes it possible to run 400B-parameter models locally. And running on a MacBook is completely silent—a stark contrast to the roaring GPU fans on Linux servers.

Common Toolchains

• Ollama: The most convenient local LLM runner
• LM Studio: GUI-based, beginner-friendly
• llama.cpp: Best performance, supports various quantizations
• vLLM: Production-grade inference serving

Limitations

• Running large models locally is still slower (3-5x slower than APIs)
• Laptops get hot running 70B+ models
• Requires a certain level of technical expertise

But the trend is clear: AI inference is shifting from "must be in the cloud" to "can be local." Just like how personal computers replaced mainframes back in the day.

References:

• Ollama Official