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Optimize LLM inference with vLLM

Ready to serve your large language models faster, more efficiently, and at a lower cost? Discover how vLLM, a high-throughput and memory-efficient inference and serving engine, is changing how enterprises deploy generative AI. In this video, Michael Goin, Red Hat Principal Software Engineer and a contributor to the vLLM project, breaks down how vLLM optimizes performance for real-world AI workloads. As generative AI moves from experimentation to production, the cost and complexity of serving large language models (LLMs) have become major roadblocks. Traditional inference methods struggle to keep up with demanding workloads, leading to slow response times and inefficient GPU utilization. Join Michael as he explains how vLLM solves these critical challenges. This video covers: ● The problem with traditional LLM serving and why it's inefficient. ● How vLLM’s core technologies deliver up to 24x higher throughput. ● The benefits of using an open source, community-driven tool for AI inference. ● How Red Hat integrates vLLM into its AI product suite for enterprise-ready deployments. Whether you're building chatbots, summarization tools, or other AI-driven applications, vLLM provides the speed, scalability, and efficiency you need to succeed. Timestamps: 00:00 - Introduction to vLLM 00:24 - What is vLLM? 01:14 - The Challenge of LLM Inference 02:08 - Core Innovations: PagedAttention, Continuous Batching, & Prefix Caching 03:29 - State-of-the-Art Performance 04:01 - Hardware and Community Support 05:02 - Red Hat's Contribution to vLLM 05:50 - Get Started with vLLM Explore how Red Hat and vLLM deliver enterprise-ready AI: 🔒 Learn more about Red Hat AI → https://www.redhat.com/en/products/ai ✨ Read the blog on vLLM → https://www.redhat.com/en/topics/ai/w... 💻 Check out the vLLM documentation → https://docs.vllm.ai/ ⭐ Star the project on GitHub → https://github.com/vllm-project/vllm #RedHat #OpenSource #vLLM