A Step-by-Step Guide to Serving Giant Language Models To Millions of Users

We’ve reached an extraordinary point with Large Language Modes (LLMs) — models exceeding hundreds of billions of parameters are now commonplace.

Have you ever paused and wondered precisely how these enormous models are served to millions of users efficiently? And what are the critical technical details behind the scenes?

I was also curious, and I decided to unpack the architecture step-by-step for you today.

Let’s see how we can handle real-world LLM inference to the masses.

Step 1: Understanding the Problem Clearly

Let’s quickly clarify the exact issues that scaling large-model inference faces:

• GPU Underutilization: GPUs often stay idle due to mismatched workloads.
• KV Cache Inefficiency: Frequent recomputation of KV caches wastes resources.
• Memory Bottlenecks: GPU memory fills up rapidly, limiting context lengths and concurrency.
• Network Latency: Data transfer across multiple GPUs and nodes introduces delays.
• Dynamic Traffic Demands: Static GPU allocations fail under changing workloads.