Uploaded August 2025 | Updated September 2026, 5 hours ago
Day 3: Quantization in Large Models by Chris De Sa.
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 (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.
Day 3: Quantization in Large Models by Chris De Sa.
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 (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.



![[Live] ScaleML Series Day 4 — Positional Encodings and PaTH Attention
Day 4: Positional Encodings and PaTH Attention by Songlin Yang.
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. [Live] ScaleML Series Day 4 — Positional Encodings and PaTH Attention](https://i.ytimg.com/vi/l6_fdwRvMPk/mqdefault.jpg)






