Co-authored PyTorch FSDP: practical lessons for scaling fully-sharded training workloads.
Researcher Profile
Ajit Mathews
Fully Sharded Data Parallel training (FSDP)
Co-author, PyTorch FSDP
Co-authored PyTorch FSDP: practical lessons for scaling fully-sharded training workloads.
Topics
About This Page
This profile is meant to help you get oriented quickly: why this researcher matters, what to read first, and where to explore next.
Last updated
March 20, 2026
Official And External Links
Known For
The ideas, systems, and research directions that make this person worth knowing.
01
Fully Sharded Data Parallel training (FSDP)
02
PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel
03
PyTorch Distributed (docs)
04
Systems
05
Training
06
Distributed Training
Start Here
Canonical papers, project pages, or repositories that anchor this profile.
Signature Works
Additional papers, projects, or repositories that help flesh out the profile.
Supporting Sources
Additional links that help verify and flesh out this profile.
Related Researchers
People worth exploring next because they share topics, labs, or source material with this profile.
Co-authored PyTorch FSDP: practical lessons for scaling fully-sharded training workloads.
Co-authored PyTorch FSDP: practical lessons for scaling fully-sharded training workloads.
Co-authored PyTorch FSDP: practical lessons for scaling fully-sharded training workloads.
Co-authored PyTorch FSDP: practical lessons for scaling fully-sharded training workloads.
Co-authored PyTorch FSDP: practical lessons for scaling fully-sharded training workloads.