"A Beginner-to-Advanced Roadmap for Learning LLM Inference Infrastructure (for Cloud, DevOps & Platform Engineers)"

If you’re learning inference infrastructure, here’s a practical roadmap you can follow (from beginner to advanced) For Cloud, DevOps, and Platform engineers, this is a natural extension of what we already do: Make systems fast, reliable, observable, and cost-efficient. The difference? Model behavior and GPU memory now shape our infrastructure decisions. BEGINNER Stage 1: Build Your GPU & Systems Foundation → Learn CPU vs. GPU architecture and why inference workloads favor GPUs → Familiarize yourself with VRAM, memory bandwidth, CUDA cores, and Tensor Cores → Get comfortable with Linux and the nvidia-smi CLI → Learn the basics of networking(inter-node + intra-node) and storage in GPU systems Stage 2: Dive Into LLM Inference → Familiarize yourself with tokens, model weights and the inference lifecycle → Break down prefill vs. decode and their different compute patterns → See how KV cache grows with context length and impacts memory INTERMEDIATE Stage 3: Analyze Inference Performance → Identify sources of latency, throughput, and memory bottlenecks → Familiarize yourself with TTFT, TPOT, and inter-token latency → Distinguish between compute-bound and memory-bound workloads → Learn GPU profiling with nvidia-smi and Nsight Stage 4: Get Hands-On With Model Serving → Deploy models with vLLM, SGLang, or TRT-LLM (locally is fine) → Dive into batching, PagedAttention, scheduling, and request queues → Benchmark how serving strategies impact latency and throughput ADVANCED Stage 5: Dive Into GPU Networking & Parallelism → Learn when a single GPU isn’t enough for a model (multi-node inference) → Understand tensor/pipeline/data/expert parallelism techiniques → Analyze how communication overhead impacts inference Stage 7: Architect Distributed Inference → Learn how data moves across GPUs and nodes → Explore disaggregated prefill and decode → Design around network and communication bottlenecks Stage 8: Production-Ready Inference Systems → Focus on autoscaling, queueing, cold starts, and cost per token affect architecture → Learn how TTFT and ITL translate into inference SLOs → Design observability for latency, throughput, GPU utilization, and errors → Explore AI gateways for routing, rate limits, fallbacks, and traffic management The best way to learn this? Build. Benchmark. Change one variable at a time. Then document the bottleneck, the tradeoff, and the result. That write-up is portfolio proof. More importantly, it’s how you go from understanding GPUs to designing dependable inference systems.

A Practical Roadmap to Learning Inference Infrastructure (Beginner to Advanced)

For Cloud, DevOps, and Platform engineers, this is a natural extension of what we already do: make systems fast, reliable, observable, and cost-efficient. The difference is that model behavior and GPU memory now shape our infrastructure decisions.