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Join the vLLM community to discuss optimizing LLM inference!
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About Event

​Join Us for the First Official vLLM Meetup in Europe!

​Hosted by Red Hat, IBM, and Mistral AI, this event takes place on 6 November 2025 in Zürich and brings together vLLM users, developers, and AI engineers to explore the latest in GenAI inference.

​Learn from the Core vLLM Team

​Hear directly from core vLLM committers and maintainers shaping the project’s roadmap and building its most advanced features. Expect deep technical talks, live demos, and plenty of time to connect with the community.

​Agenda (Subject to Change & More Awesomeness)

​5:00 PM – Doors Open & Meet the vLLM Team

​5:30 PM – Welcome & Opening Remarks

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

​Michael Goin, Lead vLLM Maintainer & Principal Engineer, Red Hat AI

​6:00 PM – From Beginner to Advanced Quantization in vLLM

​Eldar Kurtić, Principal Research Scientist, Red Hat AI
Dan Alistarh, Professor at ISTA & Researcher, Red Hat AI

​6:30 PM – Training Mistral Reasoning Models using vLLM

​Patrick von Platen, Research Engineer, Mistral AI
Julien Denize, Open Source Engineer, Mistral AI

​6:45 PM - P/D Disaggregated Serving in Production with vLLM

​Mickaël Seznec, GPU Engineer, Mistral AI
Mathis Felardos, Inference/Software Engineer, Mistral AI

​7:00 PM – Hybrid Models as First-Class Citizens in vLLM

​Thomas Parnell, vLLM Committer & Principal Research Scientist, IBM

​7:20 PM – Intro to Distributed Inference with vLLM and llm-d

​Tyler Michael Smith, Lead vLLM Maintainer & Chief Architect - Inference Engineering, Red Hat AI

​7:50 PM – 🍕🥗🍷 Q&A, Food, Drinks, and Networking 🍻 🍰

​Important Information

​Registration Deadline: Registration closes 24 hours prior to the event. We will be unable to admit any attendees who are not registered.

​Check-In: Please bring a photo ID to verify your registration upon arrival.

​We look forward to seeing you there!

Location
IBM Switzerland AG
Vulkanstrasse 106, 8048 Zürich, Switzerland
Avatar for vLLM Meetups and Events
Join the vLLM community to discuss optimizing LLM inference!
300 Went