Cover Image for Distributed Inference Meetup NYC
Cover Image for Distributed Inference Meetup NYC
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Join the vLLM community to discuss optimizing LLM inference!
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Registration
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About Event

llm-d Distributed Inference Meetup NYC

Hosted by Red Hat AI, IBM Research, and AMD, this event takes place on March 11, 2026 in New York City.

What to Expect

  • Deep technical sessions from llm-d maintainers, committers, and teams using AI at scale

  • Live demos focused on real distributed workflows

  • Great networking with food and drinks

Who Should Attend

  • 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

Meetup Agenda

*Agenda is subject to change and more awesomeness

4:30pm — Doors Open, Check-In

5:15pm — Intro to llm-d for Open Source Distributed Inference & Project Update

Carlos Costa, Distinguished Engineer, IBM Research

5:35pm — Distributed LLM Serving on AMD with llm-d

Liz Li, Product Application Engineer, AMD
Kenny Roche, Sr. Manager Software Development, ML Framework, AMD

5:55pm — The Path to Intelligent Routing: Lessons Learned Scaling Wide-EP and Mixture-of-Experts (MoE) Models

Nili Guy, R&D Manager, AI Infrastructure - Hybrid Cloud, IBM Research
Tyler Smith, Chief Architect - Inference Engineering, Red Hat AI

6:15pm — KV-Cache Wins You Can See: Prefix-Cache Scheduling, Offloading, and Scaling with llm-d

Maroon Ayoub, Staff Research Scientist, IBM Research

6:35pm — Q&A and Open Discussion

7:00pm — Pizza and Networking 🍕 🤝

8:30pm — Event Ends and Door Close

Important information

Registration closes 24 hours before the event. We cannot admit unregistered attendees.

Please bring a photo ID to verify your registration on arrival.

See you in New York City

If you are building, deploying, and scaling inference, this is the room to be in. See you soon!

Location
1 Madison Ave
New York, NY 10010, USA
Avatar for vLLM Meetups and Events
Join the vLLM community to discuss optimizing LLM inference!
112 Going