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How to run DeepSeek and other LLMs locally on your Mac
- Running large language models (LLMs) like DeepSeek, Google's Gemma, or Meta's Llama locally on your Mac is preferable for privacy, security, and better performance.
- Running LLMs locally ensures data privacy, eliminates the need for constant internet access, and allows customization of responses using proprietary data.
- Developers can benefit from running LLMs locally to experiment with AI models and create tailored tools for their work.
- To run LLMs locally on a Mac, an Apple silicon-powered Mac with at least 16GB of system memory is recommended, though 8GB can work with reduced performance.
- LLMs come in various parameters, with higher parameters requiring more storage and resources to run efficiently.
- LM Studio is a free tool that simplifies running LLMs on a Mac, allowing users to download and install models like DeepSeek or Llama.
- After downloading a chosen LLM, users can configure its settings within LM Studio and start interacting with the AI model for various tasks.
- LM Studio offers features to manage resource usage, initiate multiple conversations with LLMs, and ensure smooth performance on Mac devices.
- Even older Apple silicon Macs can run LLMs effectively with the right settings, demonstrating impressive performance capabilities.
- Regularly review and delete unused LLMs from your Mac to free up space and optimize performance for a seamless experience.
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