Training a large language model is computationally intensive, but deploying it for real-time applications presents its own set of performance challenges. The process of generating text token by token, known as autoregressive decoding, can be inherently slow and memory-hungry. Each new token generation typically requires processing the entire preceding sequence, leading to significant computational cost and latency, especially for long outputs.

This chapter focuses on practical techniques to optimize this generation process. We will cover methods to reduce redundant computations, manage memory efficiently, and improve overall throughput and latency. We will examine key strategies such as Key-Value (KV) caching to avoid recomputing attention components for previous tokens. We'll also look at optimized attention implementations like FlashAttention that minimize memory input/output operations. Furthermore, we will discuss batching techniques to enhance throughput and explore speculative decoding for faster generation. Implementing these strategies is essential for making large language models practical and cost-effective for real-world deployment scenarios.