​Deep technical sessions. Live demos. Real conversations.

​If you're deploying, or scaling LLM inference, this is the room to be in.

​Join Red Hat AI, IBM, NVIDIA, The Open Accelerator, MIT, and the vLLM community in Boston for an evening of technical depth:

• ​Hear directly from vLLM maintainers and committers

• ​See live demos of real inference workflows

• ​Learn how to put your learnings into practice with the Open Accelerator

• ​Connect with the engineers and platform teams pushing the state of the art

​Program

​Optional Pre-Event Workshop

​3:30 PM — Doors Open for Workshop Attendees

4:00–5:00 PM — Distributed Inference with llm-d: Your Production-Ready Path to Scalable LLM Inference

​About the workshop: llm-d is a distributed inference orchestration layer that reduces tail latency (P95/P99) through intelligent cache-aware routing. In this hands-on workshop, participants deploy Llama 3.1 8B with vLLM, benchmark single-GPU performance, scale to multiple GPUs with naive load balancing, and then use llm-d to demonstrate how cache-aware routing significantly reduces tail latency.

​Meetup Agenda

​5:00–5:30 PM — Doors Open, Check-In

5:30–5:40 PM — Welcome and Opening Remarks

​Saša Zelenović, Sr. Technical Marketing Manager, Red Hat AI

​5:40–6:00 PM — Intro to vLLM and Project Update

​Michael Goin, vLLM Maintainer and Principal Engineer, Red Hat AI

​6:00–6:20 PM — Getting Started with Model Compression for Fast and Efficient Inference

​Charles Hernandez, ML Engineer, Red Hat AI

​6:20–6:40 PM — Accelerating LLM Inference with Speculative Decoding

​Helen Zhao, ML Engineer, Red Hat AI
Fynn Schmitt-Ulms, ML Engineer, Red Hat AI

​6:40–7:00 PM — Agentic AI with vLLM

​Dhruv Nandakumar, Agent and Inference Engineering, NVIDIA

​7:00–7:30 PM — Tackling Distributed Inference at Scale with llm-d and Kubernetes

​Carlos Costa, Distinguished Engineer, IBM

​7:30–7:40 PM — From Meetup to Hackathon: Building Together in the Open AI Accelerator

​Stefanie Chiras, SVP, The Open Accelerator, Red Hat

​7:40–8:00 PM — Community Discussion and Q&A

​8:00–9:00 PM — Networking, Food and Drinks

​Who Should Come

• ​vLLM users and contributors

• ​ML and infra engineers working on inference and serving

• ​Platform teams running GenAI in production

• ​Anyone curious about efficient inference across local, cloud, and Kubernetes

​Before You Arrive

• ​Registration closes 24 hours before the event

• ​Unregistered attendees cannot be admitted

• ​Bring a photo ID for check-in

​See you in Boston! The inference conversation starts here.