Chapter 13: Deployment & Optimization

Training an LLM is expensive, but serving it at scale can be even more so. A single GPT-4-class model requires multiple high-end GPUs just for inference. Multiply that by thousands of concurrent users, and costs can spiral into millions of dollars per month. The gap between a working demo and a production service is enormous.

Deployment optimization operates along three axes: model size (can we make the model smaller?), inference speed (can we generate tokens faster?), and cost (can we serve more users per dollar?). These are interconnected -- a smaller model is faster and cheaper, but may sacrifice quality.

Quantization reduces model size by using lower-precision numbers (int8 or int4 instead of float16), often with minimal quality loss. Knowledge distillation trains a smaller "student" model to mimic a larger "teacher," capturing most of the teacher's capability in a fraction of the size.

On the infrastructure side, serving frameworks like vLLM and TGI maximize throughput through techniques like continuous batching, PagedAttention, and speculative decoding. Understanding the KV cache -- the memory bottleneck during generation -- is essential for optimizing inference.

Cost management requires understanding the economics: when to use large models vs. small models, how to route queries based on complexity, caching strategies, and when to fine-tune vs. prompt.

This chapter covers:

• Quantization: Reducing numerical precision to shrink models and accelerate inference
• Knowledge Distillation: Training smaller models to replicate larger model behavior
• Serving Infrastructure: Systems for high-throughput, low-latency LLM inference
• Inference Optimization: Techniques for faster token generation and better GPU utilization
• Cost Management: Strategies for economically sustainable LLM operations at scale