Support at Distributed Training of Large-Scale AI Models
Overview
This service provides expertise and hands-on technical support for designing and executing distributed training workflows for large-scale AI and language models. It addresses the challenges of training foundation and domain-specific models that exceed the capacity of single-GPU setups, including data parallelism, model parallelism, and pipeline parallelism strategies. The service covers the full reproducible workflow from environment setup and cluster configuration to training execution, logging and monitoring, checkpointing strategies, and fault recovery. It enables research groups, SMEs, and startups to leverage HPC-AI infrastructure for training large models at scale, ensuring optimal utilization of available GPU resources and minimizing time-to-convergence. The service leverages state-of-the-art distributed training frameworks and tools, focusing on the NVIDIA NeMo suite and NeMo Megatron-Bridge for large language model training, employing techniques like Fully Sharded Data Parallel (FSDP), Zero Redundancy Optimizer (ZeRO stages 1—3), and NVIDIA NCCL for high-performance inter-GPU and inter-node communication. Training workflows are configured on multi-GPU, multi-node HPC clusters using Slurm job scheduling with containerized environments (Pyxis+Enroot, Singularity/Apptainer) and RDMA/NVLink-enabled interconnects. Monitoring and profiling are supported through toolkit-integrated logging, NVIDIA DCGM, PyTorch Profiler, and W&B dashboards.
Outcome
Through bi-weekly consultations, clients devise an optimized distributed training configuration tailored to their model architecture and dataset, including parallelism strategy selection, batch size tuning, gradient accumulation settings, and checkpoint management. The service ensures fault-tolerant training with automatic checkpoint recovery, efficient resource utilization across heterogeneous GPU nodes, and reproducible training pipelines ready for production-scale model development.
Service Card
| Category | User support |
| Domain | Distributed AI Training, HPC |
| Vertical | Cross-domain |
| Target users | SMEs, Startups, Research Institutions, Industrial R&D Teams |
| Service type | Support, Consultations |
| Outputs | Optimized distributed training configurations, parallelism strategy recommendations, working training scripts, Slurm job definitions, container images, performance profiling reports, and technical documentation. |
| Requirements | Access to multi-GPU, multi-node HPC-AI infrastructure with high-bandwidth interconnects (NVLink, InfiniBand/ RDMA). Containerized execution environments (Docker, Pyxis+Enroot, Singularity/ Apptainer) for reproducibility. Clients should provide model architecture specifications, training datasets, and target performance objectives. |
| Limitations | N/A |
| Availability | Q2/2026 |
| Licensing | N/A |
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