The Art of Compressing LLMs: Pruning, Distillation, and Quantization
NVIDIA Deep Learning Institute
Last updated June 18, 2026
The Art of Compressing LLMs is an eight-hour, hands-on course from NVIDIA's Deep Learning Institute on making large language models smaller and cheaper to run without giving up much of their performance. It teaches three core techniques — pruning (removing parts of the network), knowledge distillation (training a smaller "student" model to imitate a larger "teacher"), and quantization (storing the model's numbers at lower precision) — and, importantly, how to weigh the trade-offs each one forces between accuracy, speed, cost, and hardware. Working in Python and PyTorch with tools from NVIDIA's stack, you take a model through the full pipeline from compression to deployment and benchmarking. It ends with an assessment in which you apply the techniques to a new dataset and hit a target accuracy. A focused, engineering-minded course on efficient model deployment.
What you'll learn
- Choosing the right compression method for a problem and its constraints
- Applying structured pruning to reduce model complexity
- Using knowledge distillation to transfer knowledge from a larger model to a smaller one
- Applying quantization, then benchmarking and deploying the compressed model
Frequently asked questions about The Art of Compressing LLMs: Pruning, Distillation, and Quantization
Who is The Art of Compressing LLMs: Pruning, Distillation, and Quantization for?
Developers familiar with Python, PyTorch, and transformer-based LLMs who want practical model-compression and deployment skills.
Is The Art of Compressing LLMs: Pruning, Distillation, and Quantization free?
No — The Art of Compressing LLMs: Pruning, Distillation, and Quantization is a paid course.
What are the prerequisites for The Art of Compressing LLMs: Pruning, Distillation, and Quantization?
Familiarity with Python and PyTorch, conceptual knowledge of deep learning, and familiarity with LLM architectures including transformers, attention mechanisms, and feed-forward networks.
Do you need to code for The Art of Compressing LLMs: Pruning, Distillation, and Quantization?
Yes — The Art of Compressing LLMs: Pruning, Distillation, and Quantization involves hands-on coding.
Why we suggest this course
For developers who can build models but now need to run them affordably — the skills behind fitting a capable LLM into real latency, memory, and cost budgets. The distinct takeaway is hands-on practice combining pruning, distillation, and quantization and judging the trade-offs each one demands.