Model Compression Techniques for AI Systems
Understand key model compression techniques used in AI engineering, including parameter-efficient fine-tuning methods like LoRA, various quantization approaches, and knowledge distillation. Learn how these reduce memory and compute requirements, enabling effective training and deployment of large-scale AI models even with limited hardware resources.
We'll cover the following...
- What is the difference between full fine-tuning and PEFT?
- How does LoRA work and why is it the dominant PEFT method?
- What are other PEFT methods worth knowing?
- What is quantization and how does it reduce model size?
- What is knowledge distillation?
- How would you implement a LoRA weight update from scratch?
- What’s next?
A 70B parameter model in full precision (BF16) occupies roughly 140GB of VRAM. A single H100 has 80GB. Full fine-tuning requires storing the model, gradients, and optimizer states simultaneously, which can multiply that memory requirement by 4-8x. For most teams, this is simply not an option. Parameter-efficient fine-tuning and quantization are not academic curiosities. They are the techniques that make modern AI engineering economically viable.
“Smaller” does not mean “worse.” A well-quantized Llama 3 70B at 4-bit often outperforms a full-precision Llama 3 8B on most tasks. A LoRA fine-tune of a 70B base model frequently beats a fully fine-tuned 7B model. The goal is not to make models small; it is to extract maximum quality per VRAM dollar.
What is the difference between full fine-tuning and PEFT?
Full fine-tuning updates every parameter in the model. For a 70B model, that means computing and storing gradients for 70 billion parameters, plus optimizer states (Adam requires two extra tensors per parameter for its first and second moment estimates). The total memory footprint is roughly 4x the model size for AdamW. This requires multi-GPU setups with expensive interconnects, and a single fine-tuning run can cost tens of thousands of dollars.
Parameter-efficient fine-tuning (PEFT) keeps the original model weights frozen and introduces a small number of new trainable parameters that adapt the model’s behavior. Because only the new parameters need gradients and optimizer states, the memory overhead is a tiny fraction of full fine-tuning. The key insight that makes PEFT work: the pretrained representations are already excellent. For most tasks, you do not need to change the bulk of the model. You need to redirect its existing capabilities.
How does LoRA work and why is it the dominant PEFT method?
LoRA (Low-Rank Adaptation) is based on the observation that the weight updates learned during fine-tuning ...