AI Infrastructure Explained (GPUs, vLLM, and LLM-D)

KodeKloud · 2026-08-18

AI Infrastructure Explained (GPUs, vLLM, and LLM-D)KodeKloud ▶ Play the video

Why it earned a slot

Scaling AI infrastructure is fundamentally a memory-math problem. KodeKloud breaks the request cycle into prefill (the pause) and decode (the stream), noting that batching hits a hard ceiling dictated by memory rather than raw compute. I'd argue that understanding the distinction between compute, capacity, and bandwidth is the prerequisite for anyone trying to move beyond a single-GPU setup.

The short version

Why "At Capacity" is a memory problem

Why it matters

Without prefix caching and efficient routing via tools like LLM-D on Kubernetes, expensive GPUs sit idle while users face latency spikes.

My take

I think the "prefill vs. decode" distinction is the most critical concept for developers to grasp if they want to optimize the perceived speed of their AI applications.

How it connects

• Evaluate how prefix caching affects your specific prompt latency.
• Compare the resource utilization of a basic model server versus a Kubernetes-routed fleet using LLM-D.

Bottom line

Audit your request pipeline to identify whether your bottleneck is in the prefill or decode phase.