vLLM
vLLM is an open-source library for fast and memory-efficient LLM inference and serving. It implements the PagedAttention algorithm to optimize GPU memory usage during text generation, allowing larger models to run on limited hardware. The system dramatically increases throughput while reducing latency for production LLM deployments.
Companies need vLLM expertise now because the cost of serving large language models at scale has become a major bottleneck for AI applications. With the shift from experimental models to production deployments, organizations require 2-5x better throughput to make LLM services economically viable. The recent surge in multi-modal and multi-tenant LLM applications makes efficient serving infrastructure critical for competitive AI products.
🎓 Courses
Production LLM Deployment: vLLM,FastAPI,Modal and AI Chatbot
Whether you're building apps for business, customer interaction, or personal projects, this course is your gateway to mastering AI model
AI Engineer Production Track: Deploy LLMs & Agents at Scale
2026 course covering deploying LLMs to AWS/GCP/Azure with vLLM, FastAPI, and production MLOps
📖 Books
VLLM Deployment Engineering: Production Serving, Optimization, and Scalable Model Operations (Intelligent Systems Infrastructure Series Book 1) , Ming, Alex, eBook
· 2025
Book 1 of 2 · Intelligent Systems ... Start over · Previous set of slides · vLLM in Production : Running LLMs at Scale with GPUs, High-Perform
Optimizing LLM Performance: Framework-Agnostic Techniques for Speed, Scalability, and Cost-Efficient Inference Across PyTorch, ONNX, vLLM, and More: Poisson, Peter E.: 9798294338459
· 2025
If you're building, serving, or scaling LLMs in 2025, this is the performance engineering guide you've been waiting for. Ke
vLLM in Production: Running LLMs at Scale with GPUs, High-Performance Inference & Modern AI Infrastructure: Denning, Hollis: 9798245694542
· 2025
This book is built around lab-first, failure-driven learning: Chapter-based practice labs reinforce every major concept · A full-stack capstone projec
🛠️ Tutorials & Guides
[vLLM Office Hours #38] vLLM 2025 Retrospective & 2026 Roadmap - December 18, 2025
In this vLLM Office Hours session, we looked back at everything that happened across the vLLM project in 2025 and shared a forward-looking roadmap for
State of vLLM 2025 | Ray Summit 2025
At Ray Summit 2025, Simon Mo from vLLM shares a comprehensive look at the past year of progress in the vLLM project and what’s coming next on the road
[vLLM Office Hours #37] InferenceMAX & vLLM - November 13, 2025
We dig into InferenceMAX, an open source continuous inference benchmarking framework that sweeps popular LLMs across hardware and software stacks to t
Vienna vLLM Meetup Live Stream - March 11, 2026
Tune in to the Vienna vLLM meetup live on YouTube.Agenda:• Intro to vLLM and project update• Transforming LLM quantization• A brief tutorial on specul
[vLLM Office Hours #33] Hybrid Models as First-Class Citizens in vLLM - September 25, 2025
In this session, we explored hybrid models as first-class citizens in vLLM. Michael Goin (vLLM Committer, Red Hat) shared the latest vLLM project upda
Embedded LLM’s Guide to vLLM Architecture & High-Performance Serving | Ray Summit 2025
At Ray Summit 2025, Tun Jian Tan from Embedded LLM shares an inside look at what gives vLLM its industry-leading speed, flexibility, and extensibility
@vllm_project: vLLM Production Stack now has an end-to-end deployment
Official vLLM thread on production deployment stack and benchmarks
@DailyDoseOfDS_: Learn how LLM inference actually works under the hood
Visual explanation of LLM inference optimization techniques
Learning resources last updated: March 17, 2026