AMD Advancing AI

Kunlun Zhu
Developer of OpenManusProject, Graduate Student Researcher UIUC

Learn from the founder of the OpenManus project how to develop an AI agent application on AMD Instinct™ MI300X GPU, using MCP, open-source models (Qwen3, Llama4), and the OpenManus framework in a browser-based workflow.

Kunlun Zhu is currently a first-year MSCS student at UIUC. He has been a participant in many agentic open-source projects, such as OpenManus, XAgent, ChatDev, and ToolBench, receiving over 80k stars. He has published multiple works in ICML, ICLR, ACL, TMLR, and Nature Communications, receiving over 1200 citations about Agent and tool use. He was previously working as a Research scientist at Tsinghua University and intern at CMU, Mila, and one of the early founding members of ModelBest Inc. and OpenBMB.

Matthias Reso
Partner Engineer, AI, Meta

Learn from PyTorch core contributors how to fine-tune LLMs and diffusion models with torchtune, explore benefits of finetune library like Unsloth, and work hands-on to experience the advantages of fine-tuned models.

Matthias is a Partner Engineer, AI at Meta with 12+ years of experience in computer vision and AI applications. As part of PyTorch's Applied AI team he loves to explore and overcome the boundaries of deep learning frameworks and help others to do the same. His interest span high-performance model optimization, distributed training, and inference.

Robert Shaw
Director of Engineering, Red Hat AI

Learn from one of the core maintainers of vLLM the challenges in production-scale model serving, optimize performance with deployable kernels, and apply them to an LLM to boost efficiency, gaining confidence in enhanced performance and TCO benefits.

Robert Shaw serves as the Director of Engineering at Red Hat and has also held the position of Committer at vLLM. With a focus on engineering leadership and contributions to open-source projects, Robert Shaw demonstrates a strong commitment to driving innovation and quality in software development.

Mark Saroufim
Meta

Explore GPU architecture, learn kernel development basics from the founder of GPU Mode, one of its kind community for GPU kernel development, start with a simple kernel example, and join a GPU Mode kernel project to build practical skills.

Mark Saroufim is a Software Engineer at Meta Platforms and core contributor to PyTorch, specializing in GPU optimization and performance engineering. He co-founded GPU MODE , an open-source research community with over 16,000 members dedicated to making GPU programming more accessible.

Daniel Han
CEO, Unsloth

Discover a new approach to fine-tuning LLMs from Unsloth, achieving faster training while using less memory. Learn practical techniques for optimizing models like Llama and Gemma for specific tasks.

Daniel Han is the CEO of Unsloth, an open-source AI startup on a mission to make AI accessible and accurate for everyone. Unsloth has grown rapidly, earning over 38,000 GitHub stars and surpassing 10 million monthly downloads on Hugging Face. The team has contributed critical bug fixes to major models, including LLaMA, Phi, Gemma, and Mistral. Before founding Unsloth, Daniel worked at NVIDIA, achieving a 2000x speedup in t-SNE visualization.

Chris Lattner
CEO, Modular AI, Creator of LLVM

Explore using the Mojo programming and language and MAX framework to unlock the power of AMD GPUs for leading-edge Generation AI applications.

Chris Lattner is a co-founder and the CEO of Modular, which is building an innovative new developer platform for AI and heterogeneous compute. Modular develops MAX, an AI deployment system that accelerates GenAI inference, as well as the Mojo language - a Pythonic system for GPU programming and high performance. He co-founded the LLVM Compiler infrastructure project, the Clang C++ compiler, the Swift programming language, the MLIR compiler infrastructure, the CIRCT project, and has contributed to many other commercial and open source projects at Apple, Tesla, Google and SiFive.

Simon Mo
vLLM Project Co-Lead, vLLM

Explore overcoming LLM inference hurdles with memory-efficient methods, enabling fast, scalable AI deployment for real-time applications across industries.

Simon Mo is a PhD student at Berkeley Sky Computing Lab. HHe is a co-lead of vLLM project, which focuses on building an end-to-end stack for LLM inference on your own infrastructure. The project aims to improve the throughput of popular LLMs by 2-4x with the same level of latency compared to other systems.