![Lecture 72: [ScaleML Series] Efficient & Effective Long-Context Modeling for Large Language Models @GPUMODE](https://i.ytimg.com/vi/DFcKFDt0QEg/maxresdefault.jpg)
Uploaded August 2025 | Updated September 2026, 13 hours ago
Speaker: Guangxuan Xiao (PhD at MIT)
Full Schedule: scale-ml.org/bootcamp
The GPU MODE x Scale ML speaker series is a 5-day, online event hosted on the GPU MODE YouTube channel where top researchers in AI will talk about various architectural and system-level advances that are integrated into OpenAI’s frontier open-source model, GPT-OSS.
Each day will consist of ~2 hours of talks and discussions, covering a different component of the evolving transformer stack—from quirks in the attention mechanism and positional encodings to quantization, MoEs, and custom GPU kernels.
Speaker: Guangxuan Xiao (PhD at MIT)
Full Schedule: scale-ml.org/bootcamp
The GPU MODE x Scale ML speaker series is a 5-day, online event hosted on the GPU MODE YouTube channel where top researchers in AI will talk about various architectural and system-level advances that are integrated into OpenAI’s frontier open-source model, GPT-OSS.
Each day will consist of ~2 hours of talks and discussions, covering a different component of the evolving transformer stack—from quirks in the attention mechanism and positional encodings to quantization, MoEs, and custom GPU kernels.
![Lecture 72: [ScaleML Series] Efficient & Effective Long-Context Modeling for Large Language Models](https://i.ytimg.com/vi/DFcKFDt0QEg/mqdefault.jpg "Lecture 72: [ScaleML Series] Efficient & Effective Long-Context Modeling for Large Language Models
Speaker: Guangxuan Xiao (PhD at MIT)
Full Schedule: https://scale-ml.org/bootcamp/
The GPU MODE x Scale ML speaker series is a 5-day, online event hosted on the GPU MODE YouTube channel where top researchers in AI will talk about various architectural and system-level advances that are integrated into OpenAI’s frontier open-source model, GPT-OSS.
Each day will consist of ~2 hours of talks and discussions, covering a different component of the evolving transformer stack—from quirks in the attention mechanism and positional encodings to quantization, MoEs, and custom GPU kernels.")
