Sustainability in the Digital Age: Efficient AI Techniques in the LLM Era

Course

Sustainability in the Digital Age: Efficient AI Techniques in the LLM Era

Welcome to the "Sustainability in the Digital Age" series

In an era where digital technologies are reshaping industries and daily life, the environmental impact of AI systems has become a growing concern. This course explores efficient AI methodologies to address these challenges. From deep learning model compression to low-bit quantization and collaborative inference, we delve into techniques that enhance computational efficiency and reduce energy consumption. We will also focus on low-bit quantization specifically for large language models (LLMs), showcasing cutting-edge open-source tools and models. Join us to learn how to build sustainable AI systems while pushing the boundaries of innovation.

This course is part of the Sustainability in the Digital Age series, a collaborative project between colleagues from Stanford University, SAP and the Hasso Plattner Institute.

Offered by Haojin Yang | Last updated on Jul 15, 2026 | 2023 enrollments

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  • Intermediate
  • EN
  • Certificate
Course information

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Prerequisites
  • Basic understanding of Machine Learning and Deep Learning principles
  • Proficiency in Python programming
  • Familiarity with neural networks is recommended
Knowledge

There are two preparatory course options we found, which should be suitable in preparation for this course:

Time required: The course runs for two weeks with a total workload of approximately 6-8 hours.

All learning materials (Video lectures and slides, additional reading materials and case studies, self-tests) are available from the start of the course. The final exam is activated at the end of the first week and remains open until the course ends, giving participants two weeks to complete the content and one week for the exam.

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Learning goals
  • Efficient deep learning techniques with a focus on sustainability
  • Principles of model compression and low-bit quantization
  • Collaborative inference strategies to optimize resource usage
  • Application of low-bit quantization for large language models (LLMs)
  • Overview and practical use of open-source tools for efficient AI
Requirements for Proofs of Achievement
  • Confirmation of Participation: You will automatically receive a free Confirmation of Participation once you have completed at least 50% of the content.
  • Certificate: You will receive your official openHPI Certificate by earning at least 50% of the maximum number of points from all graded assignments.

Find out more on our page for Proofs of Achievement and Guidelines.

Our teachers

Haojin Yang

PD Dr. Haojin Yang is a senior researcher and multimedia and machine learning (MML) research group leader at Hasso-Plattner-Institute (HPI). Since 2019, he has been habilitated for a professorship. His research focuses on efficient deep learning, model acceleration and compression, and AI agentic systems.