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
Efficiently Serving LLMs
Predibase teaches KV caching, continuous batching, quantization — the concepts vLLM implements. Free.
Quantization Fundamentals with Hugging Face
Understand model quantization — critical for serving LLMs efficiently with vLLM.
Efficient Deep Learning Systems
Systems-level understanding of ML inference — memory, compute, batching strategies.
📖 Books
LLM Engineer's Handbook
Paul Iusztin, Maxime Labonne · 2024
Covers LLM serving infrastructure including vLLM, quantization, and production deployment patterns.
Hands-On Large Language Models
Jay Alammar, Maarten Grootendorst · 2024
Covers inference optimization, KV caching, and how serving engines like vLLM work under the hood.
🛠️ Tutorials & Guides
vLLM Official Documentation
The primary reference — installation, serving, supported models, API. Start here.
vLLM GitHub Repository
Source code, examples, benchmarks. Understand PagedAttention by reading the implementation.
vLLM Quickstart Guide
Get a model serving in 5 minutes — offline inference and OpenAI-compatible server.
Hugging Face Text Generation Inference
Alternative serving engine to compare — continuous batching, Flash Attention, watermarking.
Best LLM Inference Engines in 2026: vLLM, TensorRT-LLM, TGI, and SGLang Compared
Yotta Labs
Comprehensive comparison with benchmarks — explains PagedAttention and when to use each engine
vLLM vs SGLang vs LMDeploy: Fastest LLM Inference Engine in 2026
PremAI Blog
H100 benchmarks showing SGLang at 16,200 tok/s vs vLLM at 12,500 tok/s
Learning resources last updated: March 30, 2026