Harnessing the Power of a Local LLM: Deploying DeepSeek with LM Studio on Your Own Computer

In this tutorial, Pablo Derendinger—CTO at e-verse, AEC Development Specialist, and Architect—walks us step-by-step through deploying and operating a locally hosted Large Language Model (LLM) using LM Studio and DeepSeek R1.

By hosting the model directly on your computer, you gain unprecedented security, full control over data, and flexibility in managing computational resources, making this method ideal for technical teams and enterprises that handle sensitive information or proprietary workflows.

The tutorial covers essential topics including the straightforward installation process of LM Studio, downloading and activating the DeepSeek R1 model, and effectively interacting with the LLM through its built-in chat interface.

Pablo delves into advanced configuration settings such as token management, real-time monitoring of hardware utilization, and deploying internal API endpoints accessible within local networks.

He emphasizes the unique advantage of DeepSeek’s transparent internal reasoning capability, allowing users to view the model’s cognitive process in real-time, enhancing understanding and accuracy of responses.

This powerful approach significantly streamlines tasks ranging from technical troubleshooting and code generation to complex parametric modeling workflows common in AEC industries.

Leveraging a locally hosted LLM like DeepSeek R1 is not only secure and efficient but also significantly reduces dependency on external services, providing superior speed and customization capabilities.

Whether you’re a BIM specialist optimizing parametric workflows, a software developer exploring AI integration, or an architect pursuing innovation, this tutorial is an essential resource. Watch the full video and discover firsthand how to harness the transformative power of local LLM deployment.