Live now: our Inference Engineering Track from AI Engineer World's Fair 2026. A benchmark tool told to run 200 queries a second that ran 38 and reported 200. A model that answered one prompt in a thousand with confident gibberish. A paper that dented memory chip stocks for a minute. - Operating Distributed Inference Systems at Scale: Nishant Gupta & Naman Ahuja, Meta - Routing LLM Inference in Production: From Engine Signals to Policy: Qianru Lao & Lu Zhang, OpenAI - Are LLM Performance Benchmarks Reliable?: Ashok Chandrasekar & Jason Kramberger, Google - Vertical Mobility: Inference from MVP to Trillion-Parameter Workloads: Sitanshu Gupta, CoreWeave - What's New in Inference Engineering: @philip_kiely, Baseten - Large clusters for small models: @svonava, Superlinked - The Frontier AI Inference Cloud for Agents: Byung-Gon (Gon) Chun, FriendliAI - KV Cache-Aware Routing and P/D Disaggregation on Kubernetes: Yuchen Fama & Ashish Kamra, Red Hat - Two Bugs That Hid in Plain Sight: A vLLM Debugging Detective Story: Asaf Gardin & @yuvalinthedeep, AI21 - Weight Folding, CUDA Streams, and the Bug That Made My Model Speak Backwards: @f_makraduli, Superlinked