Fast & Efficient LLM Inference with vLLM
by DeepLearning.AI
Compress, serve, and benchmark open-source LLMs with vLLM — the full production inference lifecycle, hands-on.
Overview
Fast & Efficient LLM Inference with vLLM is a free-to-audit short course from DeepLearning.AI in partnership with Red Hat, taught by Senior Developer Advocate Cedric Clyburn. Across roughly 1 hour 38 minutes of video and three code labs, it walks through the entire lifecycle of self-hosting an open model: compressing a Qwen model with LLM Compressor via quantization to shrink its memory footprint, serving it with vLLM through an OpenAI-compatible API, and benchmarking the deployment under realistic load with GuideLLM while evaluating quality with lm-eval. Along the way it explains the inference internals that make vLLM fast — PagedAttention for KV-cache management, prefix caching to avoid redundant computation, and continuous batching for high GPU utilization — so engineers understand the memory and throughput trade-offs behind their serving stack.
At a Glance
- Topic
- Frameworks
- Level
- Intermediate
- Format
- Course
- Cost
- Freemium
- Duration
- ~1 hour 38 minutes, self-paced (9 video lessons, 3 code examples)
- Provider
- DeepLearning.AI
- Hands-on
- Yes — code/exercises
- Certificate
- Available
What You’ll Learn
- ✓Quantize and compress an open LLM (e.g. Qwen) with LLM Compressor to reduce memory footprint
- ✓Serve a model with vLLM and call it through an OpenAI-compatible API
- ✓How PagedAttention, prefix caching, and continuous batching improve throughput and memory use
- ✓Benchmark inference under load with GuideLLM and evaluate output quality with lm-eval
- ✓Reason about KV-cache behavior and GPU memory hierarchy when tuning a deployment
Highlights
- •Built with Red Hat, the team behind much of vLLM's production tooling
- •Three hands-on labs run against a live vLLM server, not toy snippets
- •Covers the whole compress → serve → benchmark loop in under two hours
Who It’s For
Best For
- ✓AI and ML engineers deploying open-source LLMs in production
- ✓Platform and infrastructure engineers optimizing inference cost and latency
- ✓Developers moving from hosted APIs to self-hosted model serving
Prerequisites
- •Comfortable with Python
- •Basic understanding of LLMs and how transformer inference works
FAQ
What is Fast & Efficient LLM Inference with vLLM?
A hands-on DeepLearning.AI short course, built with Red Hat and taught by Cedric Clyburn, that teaches AI and platform engineers how to serve open-source LLMs efficiently with vLLM. You run the complete optimize → deploy → benchmark workflow on a live model rather than just reading about it.
Is Fast & Efficient LLM Inference with vLLM free?
Fast & Efficient LLM Inference with vLLM offers free content, with paid options for certificates or premium features.
What level is Fast & Efficient LLM Inference with vLLM for?
Fast & Efficient LLM Inference with vLLM is aimed at a intermediate audience. Recommended background: Comfortable with Python, Basic understanding of LLMs and how transformer inference works.
How long does Fast & Efficient LLM Inference with vLLM take?
Expect roughly ~1 hour 38 minutes, self-paced (9 video lessons, 3 code examples). Most learners work through it at their own pace.
What will I learn from Fast & Efficient LLM Inference with vLLM?
You'll learn: Quantize and compress an open LLM (e.g. Qwen) with LLM Compressor to reduce memory footprint; Serve a model with vLLM and call it through an OpenAI-compatible API; How PagedAttention, prefix caching, and continuous batching improve throughput and memory use; Benchmark inference under load with GuideLLM and evaluate output quality with lm-eval; Reason about KV-cache behavior and GPU memory hierarchy when tuning a deployment.