Sangkug Lym
Software Architect at NVIDIA
San Jose, California, United States
Summary
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RockstarSangkug Lym is a Software Architect and computer architect with 8 years of experience based in San Jose, California, specializing in deep learning performance and large-scale model training. He combines systems-level thinking with hands-on machine learning engineering, having contributed performance-focused improvements to NVIDIA's Megatron-LM—optimizing activation checkpointing, communication patterns, and gradient accumulation fusion for transformer training at scale. Comfortable bridging research and production, he refactors tooling and argument parsing to make large-model workflows more robust and configurable. Colleagues rely on him to diagnose bottlenecks across hardware and software stacks and to deliver pragmatic improvements that materially speed up training. An engineer who prefers measurable impact, he pairs deep technical knowledge with a knack for simplifying complex training pipelines.
9 years of coding experience
Github Skills (11)
transformers10
pytorch10
machine-learning10
deep-learning10
large-language-models10
python10
gradient9
distributed-training9
cuda9
nlp8
natural-language-processing8
Programming languages (1)
Github contributions (5)
NVIDIA/Megatron-LM
Aug 2021 - Apr 2022
Ongoing research training transformer models at scale
Role in this project:
ML Engineer
ML EngineerContributions:4 reviews, 16 commits, 3 PRs in 8 months
Contributions summary:Sangkug primarily focused on enhancing the training and functionality of large language models within the Megatron-LM framework. Their contributions include updating example scripts for model training configurations, specifically tailoring them for different model sizes and configurations related to activation checkpointing, interleaved schedules and scatter-gather communication. They also refactored and improved argument parsing within the framework, added support for persistent layer norm and incorporated gradient accumulation fusion for optimized model training.
transformer-modelstransformerslarge-language-modelsmodel-para
erhoo82/TransformerEngine
Mar 2023 - Feb 2025
A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper GPUs, to provide better performance with lower memory utilization in both training and inference.
Contributions:379 pushes, 68 branches in 1 year 11 months
floating-pointinferencenvidiatransformer-modelstransformers
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