Technical Talks

Charles Frye
Charles Frye
Developer Advocate | Modal Labs

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What Every Data Scientist Needs To Know About GPUs

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ABOUT THE TALK
  • AI Engineering

GPU Optimization for Data Scientists: Essential Knowledge from Silicon to PyTorch | Comprehensive guide to GPU architecture and optimization for modern machine learning workloads. Learn critical GPU concepts from hardware fundamentals to high-level frameworks, with focus on performance tuning for neural networks. Master practical techniques for optimizing system latency and throughput in popular ML frameworks including PyTorch, vLLM, and RAPIDS. Essential knowledge for data scientists and ML engineers working with GPU-accelerated workloads.

Charles Frye

Developer Advocate

Charles Frye

Modal Labs

Charles Frye is a Developer Advocate at Modal Labs with expertise in deep learning and neural networks. A UC Berkeley Ph.D. graduate in Neuroscience, he transitioned from biology to computer science and statistics. He previously served as a Deep Learning Educator at The Full Stack and Weights & Biases, where he created educational content on machine learning and Python programming. He's known for his talent in explaining complex quantitative concepts to non-experts.

Discover the data foundations powering today's AI breakthroughs. Join leading minds as we explore both cutting-edge AI and the infrastructure behind it. Reserve your spot at before tickets sell out!

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