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Agentic coding systems have crossed a threshold from experimentation to measurable economic impact. Their rapid adoption reveals a deeper shift: modern AI capability emerges from the co-evolution of models, training frameworks, inference engines, reinforcement systems, hardware, and cloud infrastructure, with open source enabling the flow of code, research, and operational knowledge across the stack. As performance gaps narrow and costs fall, this compounding intelligence system accelerates innovation and spreads capability across companies, industries, and hardware platforms, raising a simple question for the community: how fast do we want to evolve?
Edward Yang has worked on PyTorch at Meta since nearly the very beginning. Currently, he works on all aspects of PT2, but with a particular focus on dynamic shapes support across the stack.
VP of Product & Head of Open Source, Reflection AI
Joe Spisak is Product Director for AI at Meta with leadership roles in PyTorch, Llama and FAIR research. A veteran of the AI space with over 10 years experience, Joe led product teams at Meta/Facebook, Google and Amazon where he focused on open source AI, building developer tools... Read More →
Tuesday April 7, 2026 09:35 - 09:45 CEST Master Stage
PyTorch has evolved from a research framework into a distributed-first platform powering production AI at massive scale. As models grow to hundreds of billions of parameters, this talk explores the challenges of scaling inference across nodes and the emerging ecosystem from Monarch and TorchTitan to open, hardware-agnostic systems that makes it possible.
Corporate Vice President of AI Product Management and Ecosystem Development, AMD
Ramine Roane is the Corporate Vice President of AI Product Management and ecosystem development at AMD, based in San Jose, California. Prior to this role, he served as Vice President of Data Center Acceleration within AMD’s Adaptive and Embedded Computing Group in 2022. Before the... Read More →
Tuesday April 7, 2026 09:45 - 09:50 CEST Master Stage
Patrick von Platen is a Research Engineer at Mistral AI, focussed on
natural language processing and scalable AI systems. Currently, he
contributes to vLLM, is a former core maintainer of Transformers, and
created Diffusers.
Red Hat is shaping an open future for AI, delivering on the promise of 'Any Agent, Any Model, Any Accelerator, Any Cloud.' Discover the community advancements contributed in the PyTorch Foundation that empower enterprises to rapidly enable, test, and seamlessly scale AI workloads across their choice of infrastructure
Maryam is a Principal Engineer in Red Hat's Office of the CTO, where she focuses on standardising CPU inferencing performance evaluation to help effectively validate and scale ML workloads.
Nicolò is a Senior Machine Learning Engineer at Red Hat with a background in Deep Learning and Computer Vision. He works on Inference Optimization for vLLM, where he is a maintainer.
Tuesday April 7, 2026 10:10 - 10:15 CEST Master Stage
The discipline of evaluating large language models underwent a major transformation with the rise of general AI capabilities. Today, the field is undergoing yet another challenging transformation following the groundbreaking improvements in agentic tasks, which expect models and systems to plan and take autonomous actions in the real world. Measuring how well models and systems perform in such tasks is however still i) fragile from a methodological perspective, and ii) difficult to scale and generalize across different domains. This talk will first discuss common challenges in reproducing agentic evaluations, including differences in reference implementation, error handling, trajectory post processing, and tooling definitions. Next, it will cover infrastructural requirements that need to be addressed for such evaluations to run efficiently at scale. Finally, we will conclude with a set of (still nascent) best practices that can help alleviate “lightness” and build more consistent measurement pipelines.
Besmira Nushi is a Senior AI Research Manager at NVIDIA in Zurich, where she leads research on LLM evaluation, model analysis and generalization, and real-world and agentic AI system measurements. Previously, she spent 7+ years at Microsoft Research advancing responsible AI, model... Read More →
Tuesday April 7, 2026 10:15 - 10:25 CEST Master Stage
