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How to Set Up Your Personal AI Assistance on a MacBook For Free

How to Set Up Your Personal AI Assistance on a MacBook For Free

Murat AYDIN's avatar
Murat AYDIN
May 02, 2025
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Android Devs Substack
Android Devs Substack
How to Set Up Your Personal AI Assistance on a MacBook For Free
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Whether you are a power user or a developer, AI is now in everywhere in daily life of every person. Normally, setting up an AI assistant on your laptop is not possible but with the DeepSeek-R1, it is possible. DeepSeek R1 is an ChatGPT alternative, created by a Chinese company. DeepSeek R1 use significantly less resources comparing to other LLMs. When you install a LLM locally on your laptop, you have some advantages:

  1. It runs locally on your MacBook. You even don’t need an internet connection to run it.

  2. Your data or chat is safe, it is not transferred to a server. It is good for you if privacy is important for you.

  3. It is free. Many online AI tools are free, but there is no guarantee that it will be free forever.

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Photo by Google DeepMind on Unsplash

We will use LM Studio for running the DeepSeek-R1 on Macbook. You can download LM studio from here. Then double click the dmg file and install it.

In LM Studio, click the search icon and enter discover section:

Discover screen will display all available LLM models that can be downloaded. Write deepseek in search bar:

DeepSeek R1 models will be listed. You can choose <= 8B model if you have M2/M3/M4 MacBook Pro (16GB RAM+) system requirements. The size of the model also shows its performance and computational complexity. When the size increase, its demand for system requirements also increase.

Then choose a model and click download. Once downloaded, you can click Load Model on the download completed popup.

Or you can click select a model to load button in top of the screen in chat section:

Here select the model you downloaded and click load model. Now your personal AI assistant is ready to use. Click + button and start a new chat:

You can also use these LLM models via API calls. Thats topic of another blog post.

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