Having constructed a foundational Transformer, the next step is to understand how to effectively increase its size. Simply making the model bigger isn't always the optimal path; specific architectural choices significantly influence performance, training stability, and computational requirements as models grow.

This chapter focuses on the design decisions involved in scaling Transformer models. We will look at:

• Empirical scaling laws that relate model size, dataset size, and compute to performance, often expressed as $Performance \propto Compute^{\alpha} Data^{\beta} Size^{\gamma}$.
• The impact of increasing model depth (number of layers) versus width (hidden dimension size).
• How different activation functions like GeLU or SwiGLU compare within the feed-forward networks.
• The consequences of placing layer normalization before or after residual connections (Pre-LN vs Post-LN).
• An overview of sparse attention mechanisms designed to address the quadratic complexity of self-attention in very long sequences.

By the end of this chapter, you'll have a clearer understanding of the architectural levers available when designing larger, more capable Transformer models and the trade-offs associated with each choice.