LLM Optimization: From Research to Production

A Comprehensive Guide for Engineers Building Real-World Systems Introduction If you've deployed machine learning models to production, you know the drill: train for accuracy, then fight to make it run fast enough. LLMs amplify this challenge by orders of magnitude. Here's the reality most tutorials won't tell you: Model A might achieve 92% accuracy but takes 4 seconds per token and needs 80GB of memory. Model B scores 89% accuracy, runs in 200ms, and fits on a single GPU. In production, you're deploying Model B every single time. This isn't about compromising quality—it's about understanding that responsiveness and efficiency aren't optional features; they're production requirements. Let's dive into how the industry actually optimizes LLMs for real-world use. Why Traditional Optimization Thinking Fails for LLMs Before LLMs, optimization meant pruning decision trees or quantizing computer vision models. The playbook was straightforward. LLMs broke that playbook entirely. The fundamental