Fast & Efficient LLM Inference with vLLM Course Launch

vLLM has partnered with DeepLearning.AI and Red Hat to release a new hands-on course, "Fast & Efficient LLM Inference with vLLM," designed to teach the end-to-end lifecycle of optimizing, deploying, and benchmarking large language models (LLMs). The course addresses the technical challenges of deploying open-source LLMs with low latency and reasonable cost.

Course Objectives and Curriculum

The course is structured around a professional deployment workflow, moving from model compression to serving and final benchmarking to help learners manage the speed-cost-accuracy tradeoff. It begins with foundational concepts regarding inference and memory before transitioning into practical code examples.

Foundational Concepts and Visualizations

The curriculum emphasizes visual learning to explain the internal mechanics of LLM inference, including:

• Transformer Architecture: Analysis of token flow, layer-by-layer computation, and identification of primary bottlenecks.
• KV Cache: Visualizations of how the Key-Value (KV) cache resides in GPU memory, its growth during autoregressive generation, and the resulting memory pressure when handling concurrent users.
• Quantization: Visual explanations of the transition from default FP16 weights to INT8 or INT4, detailing the specific benefits and tradeoffs of weight-only versus weight-and-activation quantization.
• Optimization Techniques: Explanations of why continuous batching, PagedAttention, and prefix caching are critical for efficiency.

Hands-on Learning Modules

The course features three primary stages, each paired with a hands-on lab in a JupyterLab environment using live vLLM servers and actual models.

Model Compression

Learners use LLM Compressor to quantize a full-precision Qwen model. The lab focuses on comparing model size before and after quantization and measuring perplexity to quantify the impact on accuracy, demonstrating how to reduce GPU memory requirements.

Model Serving

This module covers deploying models using vLLM and interacting with them via an OpenAI-compatible API. Learners monitor vLLM metrics to observe continuous batching in action, track memory utilization changes during concurrent requests, and see how prefix caching eliminates redundant computation for shared system prompts.

Deployment Benchmarking

Using GuideLLM, learners simulate realistic traffic patterns to measure throughput and latency under load. The final step involves using lm-eval to evaluate model quality, ensuring the compressed model maintains required accuracy levels before making deployment decisions.

Course Specifications

• Title: Fast & Efficient LLM Inference with vLLM
• Instructor: Cedric Clyburn (Senior Developer Advocate at Red Hat)
• Duration: Approximately 1.5 hours, consisting of 9 video lessons and 3 hands-on code labs.
• Level: Intermediate (requires familiarity with Python and basic LLM concepts).
• Cost: Free on DeepLearning.AI.

"Deploying open-source LLMs efficiently, for many users, with low latency and reasonable cost, is challenging. This course shows you how." — Andrew Ng