Chapter 13: Deployment & Optimization
Master the engineering challenges of running LLMs in production. Learn quantization to shrink models without losing quality, knowledge distillation to create smaller student models, serving infrastructure for high-throughput inference, optimization techniques for latency and throughput, and cost management strategies for sustainable LLM operations.
Chapter Overview
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
Chapter Roadmap
Click any topic to jump in
Quantization
Reducing numerical precision to shrink models from 140GB to 35GB with minimal quality loss.
Knowledge Distillation
Training smaller student models to replicate larger teacher model behavior.
From model compression to serving at scale
Serving Infrastructure
KV cache, continuous batching, vLLM, and PagedAttention for high-throughput inference.
Inference Optimization
Speculative decoding, FlashAttention, model parallelism, and prefix caching for speed.
Cost Management
Model routing, caching strategies, and fine-tuning economics for sustainable operations.
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