Uploaded February 2025 | Updated September 2026, 9 hours ago
Speaker: Ali Hassani
Slides: docs.google.com/presentation/d/1n5nZU4n2uS-1gI_YRQorkhpzEy8_EapLI3juWfacbUA/edit
Speaker: Ali Hassani
Slides: docs.google.com/presentation/d/1n5nZU4n2uS-1gI_YRQorkhpzEy8_EapLI3juWfacbUA/edit
![[Live] ScaleML Series Day 2 — Efficient & Effective Long-Context Modeling for Large Language Models](https://i.ytimg.com/vi/PKYvAc9UZhk/mqdefault.jpg "[Live] ScaleML Series Day 2 — Efficient & Effective Long-Context Modeling for Large Language Models
Day 2: Efficient & Effective Long-Context Modeling for Large Language Models by Guangxuan Xiao.
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 (around noon PST, may start at slightly different times each day so please check frequently), 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 74: [ScaleML Series] Positional Encodings and PaTH Attention](https://i.ytimg.com/vi/QXbXdN3KIcY/mqdefault.jpg "Lecture 74: [ScaleML Series] Positional Encodings and PaTH Attention
Speaker: Songlin Yang (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 (around noon PST, may start at slightly different times each day so please check frequently), 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.")
