Fast & Efficient LLM Inference with vLLM
by DeepLearning.AI
Quantize, serve and benchmark one real model end to end on the open-source vLLM stack.
Overview
Fast & Efficient LLM Inference with vLLM is a short course DeepLearning.AI built in partnership with Red Hat and taught by Cedric Clyburn, a Senior Developer Advocate at Red Hat working on the vLLM and LLM Compressor projects. It runs about 1 hour 38 minutes across nine video lessons and three JupyterLab code labs, and it is structured as one continuous optimize-deploy-benchmark workflow on a single model rather than a tour of features. Lessons two through four cover why efficient deployment matters, inference and memory fundamentals, and optimization fundamentals. Lesson five has you quantize a full-precision Qwen model with LLM Compressor, compare model size before and after, and measure perplexity so the accuracy cost is a number instead of a claim. Lessons six and seven serve that model with vLLM through its OpenAI-compatible API while continuous batching, PagedAttention and prefix caching are visible in live metrics. Lesson eight puts the deployment under simulated traffic with GuideLLM to read time-to-first-token, inter-token latency and throughput against a service level objective, then re-checks task quality with lm-eval. The entire stack is open source and is the same one Red Hat ships commercially, so the workflow transfers directly to a self-hosted deployment. Enrollment is free; the graded quiz and certificate of accomplishment require DeepLearning.AI Pro.
At a Glance
- Topic
- Models
- Level
- Intermediate
- Format
- Course
- Cost
- Free
- Duration
- ~1 hour 38 minutes, self-paced (9 video lessons + 3 hands-on code labs)
- Provider
- DeepLearning.AI
- Hands-on
- Yes — code/exercises
- Certificate
- Available
What You’ll Learn
- ✓Quantize a full-precision Qwen model with LLM Compressor and compare model size before and after
- ✓Quantify the real accuracy cost of quantization by measuring perplexity on the compressed model
- ✓Serve a model with vLLM behind its OpenAI-compatible HTTP API endpoint
- ✓Explain how PagedAttention and continuous batching raise GPU memory utilization and throughput
- ✓Use prefix caching to eliminate redundant prefill work across repeated prompt prefixes
- ✓Benchmark a live deployment under simulated load with GuideLLM for latency and throughput
- ✓Validate post-quantization task quality with lm-eval before promoting a deployment to production
Highlights
- •Built with Red Hat and taught by an engineer who works on vLLM and LLM Compressor themselves
- •Runs one continuous optimize-deploy-benchmark loop on a single model instead of three disconnected demos
- •Uses the production open-source stack — vLLM, LLM Compressor, GuideLLM, lm-eval — not toy wrappers
- •Turns the accuracy/speed/cost tradeoff into measurements: perplexity, TTFT, inter-token latency, throughput
- •Under two hours, so it fits in an afternoon before a real self-hosting decision
Who It’s For
Best For
- ✓Platform and MLOps engineers self-hosting open-weight models on their own GPUs
- ✓AI engineers deciding between a hosted inference API and an in-house serving stack
- ✓Teams trying to cut GPU spend without guessing at the resulting quality loss
Prerequisites
- •Comfortable with Python and running code in Jupyter notebooks
- •Basic LLM concepts: tokens, context windows, what an inference call costs
- •No prior vLLM, CUDA or quantization experience assumed
FAQ
What is Fast & Efficient LLM Inference with vLLM?
A short DeepLearning.AI course built with Red Hat that walks the complete optimize-deploy-benchmark loop for self-hosted LLM inference. You quantize a Qwen model, serve it with vLLM behind an OpenAI-compatible API, and load-test the result. Afterwards you can stand up your own inference endpoint and defend the accuracy, latency and cost tradeoffs you chose with measurements rather than guesses.
Is Fast & Efficient LLM Inference with vLLM free?
Fast & Efficient LLM Inference with vLLM is free to access.
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 and running code in Jupyter notebooks, Basic LLM concepts: tokens, context windows, what an inference call costs, No prior vLLM, CUDA or quantization experience assumed.
How long does Fast & Efficient LLM Inference with vLLM take?
Expect roughly ~1 hour 38 minutes, self-paced (9 video lessons + 3 hands-on code labs). Most learners work through it at their own pace.
What will I learn from Fast & Efficient LLM Inference with vLLM?
You'll learn: Quantize a full-precision Qwen model with LLM Compressor and compare model size before and after; Quantify the real accuracy cost of quantization by measuring perplexity on the compressed model; Serve a model with vLLM behind its OpenAI-compatible HTTP API endpoint; Explain how PagedAttention and continuous batching raise GPU memory utilization and throughput; Use prefix caching to eliminate redundant prefill work across repeated prompt prefixes; Benchmark a live deployment under simulated load with GuideLLM for latency and throughput; Validate post-quantization task quality with lm-eval before promoting a deployment to production.
Topics
Sources
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