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AI Engineer World's Fair 2026 推理工程专场直播议程
AI Engineer 发布其 2026 世界大会推理工程专场的直播议程，涵盖 Meta 的规模化分布式推理、OpenAI 的生产环境 LLM 路由、Google 的 LLM 基准可靠性、CoreWeave 的万亿参数推理负载、Baseten、Red Hat 的 KV Cache 感知路由与 P/D 分离、AI21 的 vLLM 调试等演讲。帖子还提到基准工具虚报吞吐、模型偶发自信胡言等案例。
AI推荐理由 AI Engineer World's Fair 2026 推理工程专场直播的议程帖，列出 Meta、OpenAI、Google、CoreWeave、Baseten、Red Hat、AI21 等公司的具体演讲题目，涉及分布式推理、生产路由、基准可靠性、KV Cache 路由等真实工程议题，信息密度较高，但仅为议程预告，无实质技术内容。
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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.
[youtube.com/watch?v=7c9FSU…](https://www.youtube.com/watch?v=7c9FSUVcXR0&list=PLcfpQ4tk2k0W3_JZkL2SRVKTVOj7DoWtZ)
- 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](https://x.com/philip_kiely), Baseten
- Large clusters for small models: [@svonava](https://x.com/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](https://x.com/yuvalinthedeep), AI21
- Weight Folding, CUDA Streams, and the Bug That Made My Model Speak Backwards: [@f\_makraduli](https://x.com/f_makraduli), Superlinked
