Foundations of LLM Mastery: Fine-tuning on one GPU

Fine-tuning Large Language Models (LLMs) often requires substantial resources, yet recent advancements allow for effective training on a single GPU.

This practical 3.5-hour course introduces participants to efficient techniques for fine-tuning LLMs without sacrificing model performance.

In this course, participants will learn about:

• Quantization: Techniques to reduce model memory requirements, enabling the deployment of LLMs on resource-constrained hardware.
• Parameter-Efficient Fine-Tuning (PEFT) with LoRA: Fine-tune large models by adjusting a limited set of parameters, reducing computational load while achieving high-quality results.

Through hands-on exercises and guided demos, participants will gain practical experience in fine-tuning LLMs using these methods, setting the stage for immediate application in real-world projects, from question answering chat bots in an online shop to digital layers interpreting the national law in the context of a specific case. The possibilities are endless.

By the end of this course, participants will have developed a foundational understanding of techniques reducing the memory footprint of a large language model during fine-tuning and inference.

Agenda & Content

For a detailed timetable and additional information, please see Agenda & Content in the left menu.

Target audience, eligibility & prices

Everyone is welcome who wants to get a hands-on introduction to fine-tuning LLMs on one GPU.

This course is open and free of charge for all participants from academia, industry, and public administration from EU and/or EuroHPC JU member countries.

Entry level & prerequisites

Intermediate – no previous experience with fine-tuning LLMs is required.

Participants are expected to have basic programming skills in Python.   
Having worked with Pytorch would be beneficial.

Course format

This course will be delivered as a LIVE ONLINE COURSE (using Zoom).

Hands-on labs

All participants will get a temporary user account on the LEONARDO supercomputer to do the hands-on labs.

You will use your own laptop or workstation to connect conveniently from your browser to a Jupyterlab session on LEONARDO to do the hands-on exercises.

Lecturer

Simeon Harrison (AI Factory Austria AI:AT)

Martin Pfister (Advanced Computing Austria ACA GmbH)

Language

English

Date, time, and location

24.03.2026, 09:00 – 12:30 (CET), LIVE ONLINE COURSE (Zoom)

Registration

Registration is required, the registration form can be found on top of this page in the menu on the left.

Please register with your official institutional email address to prove your affiliation.

You will get an automatic confirmation by email (subject starting with "[Indico] Registration"), please check your Spam/Junk folders.

Following your successful registration, you will receive further information a few days before the course.

Please do not hesitate to contact us at training@ai-at.eu if you have any questions.

Waitinglist

After the number of registrations has reached its maximum or the registration form has been closed, you may want to send us an email (training@ai-at.eu) stating that you are interested to be put on the waiting list (vacancies may occur due to cancelations, etc.).

To be able to do the hands-on labs on the ASC systems please provide your full international mobile-phone number for the two-factor authentication required to login to the ASC systems.

Modification, withdrawal & no-show policy

Your registration is binding. Please only register for the course if you are really going to attend.

You can update your registration data or withdraw your registration anytime before the registration form has been closed via the link "Manage my registration" which you can find at the bottom of your automatic email confirmation (subject starting with "[Indico] Registration").

Alternatively, or after the registration form has been closed, please inform us about your cancelation or any change in your registration data (especially your mobile-phone number) via email (training@ai-at.eu).

No-show policy: If you do not cancel and do not show up at the course you will be blacklisted and excluded from future training events.

Organizers

This course is jointly organized by AI Factory Austria AI:AT and Austrian Scientific Computing ASC (aka ASC Research Center, TU Wien).

Acknowledgements

AI Factory Austria AI:AT has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 101253078. The JU receives support from the Horizon Europe Programm of the European Union and Austria (BMIMI / FFG).