Join us at the "Scaling AI Infra - GPUs, Kernels, LLMs and More" meetup, where leading experts from LinkedIn, NVIDIA, Sglang, and Microsoft will converge to share their groundbreaking work in large-scale AI infrastructure. This event is a unique opportunity to dive deep into the cutting-edge technologies powering the next generation of AI, from the intricate details of GPU optimization and kernel development to the latest advancements in large language model (LLM) training. Whether you're an AI researcher, engineer, or enthusiast, this meetup will offer valuable insights into the tools and techniques that make massive-scale AI possible.
Animesh is a prominent leader in AI and machine learning. He has a strong background in developing large-scale AI infrastructure, with previous roles as IBM’s CTO for Watson AI and ML Open Technology. Animesh is also a co-founder of the Kubeflow project and has contributed extensively to AI research and platform development, holding numerous patents and publications in the field.
Byron (Pin-Lun) Hsu is a Senior Software Engineer at LinkedIn, where he leads kernel optimization and distributed training for large-scale GPU clusters. He is known for developing Liger-Kernel and contributing to the optimization of LLM training. Byron is a committer to multiple open-source projects, including Flyte and the Apache Software Foundation.
Guanhua Wang is a Senior Researcher in the DeepSpeed team at Microsoft, specializing in optimizing large-scale deep learning models. He holds a PhD in Computer Science from UC Berkeley, where he was advised by Ion Stoica. His work includes innovations like ZeRO++, improving the efficiency of large model training​
Lianmin Zheng is a technical staff member at xAI, focusing on scalable AI systems, large language models, and distributed systems. He co-founded LMSYS.org and led major open-source projects like Vicuna and Chatbot Arena. His work has been widely adopted, with millions of downloads. Lianmin earned his Ph.D. from UC Berkeley and has received prestigious awards including a Meta Ph.D. Fellowship, IEEE Micro Best Paper Award, and an a16z open-source AI grant.
Ying Sheng is a co-founder of LMSYS Org, which focuses on open research and open-source projects for large model systems. Her recent research focuses on efficient inference and serving of LLMs in different scenarios. She is currently building SGLang, which is a serving framework for large language models and vision language models text.
Pradeep is specializing in high-performance GPU computing and CUDA development. He is a key contributor to the CUTLASS project, focusing on optimizing matrix multiplication and other essential computations for deep learning and high-performance computing. Pradeep has extensive experience in parallel computing architecture and is deeply involved in advancing GPU-based technologies.