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Building Sovereign AI: Scaling 100B+ Model Training on NVIDIA Blackwell Infrastructure (Presented by Lablup, Inc.)#

GTC 2026 digest Program Core pick San Jose · 16–19 March 2026

Abstract Talk page Recording

Jeongkyu Shin (Lablup, Inc.) — Theater Talk, Wednesday, March 18

Play the recording: Building Sovereign AI: Scaling 100B+ Model Training on NVIDIA Blackwell Infrastructure (Presented by Lablup, Inc.)

  • Why: The most concretely operational talk in the pre-selection: named failure modes (NCCL timeouts, 10x storage degradation from an NFS driver bug, MXFP8 precision instability on Blackwell) and network tuning (RoCE and InfiniBand) on a 500+ B200 national-scale cluster.
  • What: Hands-on experience training 100B+ parameter models from scratch on more than 500 B200 GPUs in a sovereign AI initiative: fault-tolerant orchestration, storage I/O optimization, NCCL tuning, automated failure recovery, resolution of a 10x storage slowdown traced to NFS driver issues, MXFP8 stability work, and RoCE/InfiniBand network tuning done through cross-vendor collaboration. (from abstract only)
  • How: A resilient stack with automated failure recovery, exploiting LLM training's parallelism characteristics for large-capacity fault tolerance and data-driven fault demarcation and localization.
  • Where applicable: Lablup's deployment in a Korean national sovereign-AI initiative; NVIDIA Blackwell with both RoCE and InfiniBand fabrics present. The failure-and-fix detail is what makes it valuable; scale (hundreds, not tens of thousands, of GPUs) makes it relatable to enterprise builds.