GPU Server and LLM Implementation
Learn to set up GPU servers, implement local language models, and build AI applications. Master open-source model deployment and agent integration.
Learn to set up GPU servers, implement local language models, and build AI applications. Master open-source model deployment and agent integration.
This comprehensive course explores GPU server implementation and Local Language Models (LLMs). Students learn to navigate the GPU market, set up virtual machines with GPU capabilities, and implement local LLM servers. The curriculum covers practical aspects of downloading and deploying open-source models, building Python applications powered by LLMs, and designing with AI agents. Whether you're a developer, data scientist, or AI enthusiast, you'll gain hands-on experience in creating and managing local AI infrastructure for intelligent applications.
Instructors:
English
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What you'll learn
Understand GPU market dynamics and infrastructure requirements
Set up and configure GPU-enabled virtual machines
Implement local LLM servers using open-source models
Develop Python applications powered by local LLMs
Master LLM Studio setup and configuration
Create and deploy AI agents for practical applications
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 4 modules in this course
This course provides a comprehensive introduction to GPU server implementation and Local Language Models. Students learn about the current GPU market landscape, infrastructure setup, and practical applications of LLMs. The curriculum covers essential topics including virtual machine configuration, open-source model deployment, local server implementation, and AI agent integration. Through hands-on exercises, participants develop the skills needed to build and manage their own AI infrastructure for various applications.
Intro and set up
Module 1
Large Language Model server setup
Module 2
Building a local chat application
Module 3
Using AutoGen
Module 4
Fee Structure
Instructor
Adjunct Professor
He is a graduate of West Point, where he earned a degree in Electrical Engineering and participated in track. He served as an Engineer Officer in the Army for ten years before being medically retired. During his active duty, he collaborated with the Johns Hopkins Applied Physics Laboratory to design a laser target location module, served as a NATA Company Trainer in Germany, and commanded a Basic Training Company.
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