Successfully training a large language model often results in models with billions of parameters. While powerful, these models present significant challenges for practical deployment due to their large memory footprint (requiring substantial $VRAM$) and high computational cost during inference, which translates to latency and operational expense.

This chapter addresses these challenges by introducing model compression techniques. These methods aim to reduce the size and computational demands of LLMs, making them more feasible to deploy, particularly in resource-constrained environments or applications requiring low latency.

You will learn about several key strategies:

• Quantization: Techniques for representing model weights and potentially activations using lower-precision numerical formats (like $INT8$ or $FP4$) instead of the standard 32-bit floating point ($FP32$). We'll cover post-training quantization (PTQ) and quantization-aware training (QAT).
• Pruning: Methods for removing redundant parameters or connections within the neural network, effectively sparsifying the model. This includes unstructured (individual weights) and structured (neurons, attention heads) pruning.
• Knowledge Distillation: A process where a smaller "student" model is trained to mimic the output behavior or internal representations of a larger, pre-trained "teacher" model.

We will examine the mechanisms behind each technique, discuss implementation considerations, and analyze the inherent trade-offs between the degree of compression achieved and the potential impact on model performance metrics.