MLOps
Model Serving & vLLM
High-throughput LLM inference, PagedAttention, and GPU memory optimization.
Serving modern LLMs in production requires maximizing GPU compute utilization and handling bursty real-time concurrency. vLLM and Triton Inference Server implement PagedAttention to eliminate memory fragmentation, alongside continuous batching to deliver low-latency tokens.
Virtual Lab Ready
Automated testing sandbox running in your browser.
Launch First Lab ⚡No local installation required
Hands-on Practice
Interactive Terminal Labs
Real environment tasks with automated validation. Write real commands, verify state, and debug errors.
Configure a vLLM Model Serving Daemon
Topic: High-Throughput Inference with vLLM
Construct a production configuration file for a vLLM serving container with GPU memory utilization and tensor parallel settings.
Key Checks:
- Directory ~/serving exists
- File ~/serving/vllm-config.yaml exists
Serve the Model on a Port
Topic: The Serving Stack
Build a vLLM serving image and run it published on host port 8000 — the last hop between a model sitting on disk and a model answering HTTP requests.
Key Checks:
- A Dockerfile exists in ~/serving-stack
- An image vllm-serve:v1 is built
Autoscale the Serving Tier
Topic: Scaling & Rollouts
Model servers are slow to start and expensive to keep warm. Write the autoscaler that scales them on GPU utilisation instead of on request count.
Key Checks:
- Directory ~/serving exists
- File ~/serving/hpa.yaml exists
Roll Out a New Model Gradually
Topic: Scaling & Rollouts
A new model goes live to a slice of the traffic first, with a rule that sends it back if the metrics move the wrong way.
Key Checks:
- File ~/serving/canary.yaml exists
- The file names the stable and the canary version
System Design
PagedAttention & Continuous Batching
Understand how this tool operates internally before running production workloads.
Internal Mechanics
Instead of allocating contiguous static VRAM blocks for key-value caches, vLLM manages KV caches like OS virtual memory pages. New incoming requests are spliced dynamically into running batch iterations.
Mental Model Rule of Thumb
Never treat Model Serving & vLLM as a black box. Understanding its boundary with the kernel, filesystem, and network stack prevents 90% of production outages.
Core Architectural Subsystems
PagedAttention Engine
Partitions KV cache into non-contiguous physical memory blocks.
Continuous Scheduler
Dynamically merges new prompt prefill tokens with active decode iterations.
Tensor Parallelism
Splits model layer weights across multiple GPUs using NCCL communication.
OpenAI-Compatible API
Exposes standard /v1/chat/completions endpoint for seamless integration.
Developer Reference
Command Reference & Cheatsheet
Curated commands, flags, and one-liners tested for everyday operational productivity.
vllm serve meta-llama/Llama-3.1-8B-Instruct --port 8000 --gpu-memory-utilization 0.90Launch vLLM server with 90% GPU memory allocation
vllm serve mistralai/Mistral-7B-v0.1 --tensor-parallel-size 2Shard model inference across 2 GPUs in parallel
curl http://localhost:8000/v1/modelsCheck live serving health and list loaded model weights
Fundamental Primitives
Key Terminology & Primitives
Core abstractions you must master to communicate and build with confidence.
Time to First Token (TTFT)
Latency required to process the input prompt (prefill phase) and generate the very first token.
Time Per Output Token (TPOT)
Latency between subsequent generated tokens during the autoregressive decode phase.
KV Cache Fragmentation
Memory wasted by standard inference engines pre-allocating max context lengths.
Quantization (AWQ / GPTQ / FP8)
Reducing model weight precision from FP16 to 8-bit or 4-bit to halve memory bandwidth requirements.
Production War-Stories
Common Pitfalls & Debugging Playbook
The exact errors that take down production systems and how to diagnose and resolve them.
CUDA Out of Memory on heavy traffic spikes
Symptom
Engine crashes with RuntimeError: CUDA out of memory during decode.
Resolution
Tune --max-num-seqs and --max-model-len. Ensure --gpu-memory-utilization leaves enough headroom for activation buffers.
Reliability Standards
Production Readiness Checklist
Review this checklist before deploying workloads to staging or production clusters.
- ✓Benchmark TTFT and throughput (tokens/sec) under simulated production concurrency.
- ✓Deploy with autoscaling based on queue depth and GPU memory saturation.
- ✓Enable prefix caching for applications with repeated long system prompts.
- ✓Implement model warmup requests before opening endpoints to user traffic.
Full Curriculum Track
Master Model Serving & vLLM as part of the MLOps Engineer path
Tools do not exist in isolation. Learn how Model Serving & vLLM connects with other systems, CI/CD pipelines, and infrastructure patterns.