- Distributed Training Configuration: Support for configuring distributed training environments and workflows for large-scale AI models.
- Performance Analysis: Analysis of training performance and computational efficiency on HPC infrastructures.
- Pre-training and Fine-tuning Support: Support throughout the transition from pre-training to fine-tuning, up to a validated model ready for integration.
Key Value: Enabling organizations to train and adapt large-scale AI models efficiently on HPC infrastructures.
Practical example
Companies seeking to build or adapt large-scale models require specialized architectures to ensure efficiency.
This service facilitates, among other activities:
- HPC Strategy Selection: Determining the optimal balance
between fine-tuning, RAG, or distillation based on specific model size and available data. - Parallelization Management: Implementing advanced schemes for distributed training across thousands of GPU cores without manual infrastructure testing.
- Validated Benchmarking: Ensuring models reach a “ready-for-integration” state through rigorous performance analysis and computational tuning.
Case Study: The service supports a global enterprise in selecting a
performant software stack for multimodal model training. This
avoids costly trial-and-error with parallelization schemes and
ensures the final model meets all business-specific performance
requirements on HPC hardware.
