Module · MLOps Engineer

Model Serving

Serving models as dependable APIs.

3 topics4 interactive tasks

Tool Deep-Dive Available

View full architecture diagrams, CLI cheatsheets, and production gotchas.

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TOPIC 01

High-Throughput Inference with vLLM

PagedAttention, KV cache management, and continuous batching architectures for serving LLMs.

High-Throughput Inference with vLLM

Serving Large Language Models in production requires managing massive GPU memory footprints while minimizing Time to First Token (TTFT) and maximizing tokens per second per dollar.

The Memory Bottleneck: KV Cache

During autoregressive generation, keys and values computed for previous tokens must be stored in GPU VRAM (the KV cache). Standard implementations suffer from severe fragmentation (up to 60–80% wasted memory).

PagedAttention Architecture

vLLM solves memory fragmentation by adapting virtual memory paging from operating systems:

Key-value vectors are split into fixed-size physical memory pages.
Non-contiguous physical memory blocks are linked via page tables.
Enables continuous batching: new incoming requests can be joined into existing running batches dynamically without waiting for sequences to finish.

Hands-On Tasks in This Topic (1)

intermediate

Practice Task

Configure a vLLM Model Serving Daemon

Construct a production configuration file for a vLLM serving container with GPU memory utilization and tensor parallel settings.

Environment: ubuntu:24.04Launch Lab ⚡

TOPIC 02

The Serving Stack

Build a serving image and put it on a port, like production does.

What you'll practice

Serve the Model on a Port — practice · intermediate

A command you'll meet

docker run -d -p 8000:80 vllm-serve:v1

-p 8000:80 publishes the container on host port 8000 — the last hop between a model sitting on a disk and a model answering requests. Open the task below for the full walkthrough.

Hands-On Tasks in This Topic (1)

intermediate

Practice Task