Model Parallelism
Splits large transformer models across multiple GPUs to enable training of models with billions of parameters without memory bottlenecks.
Pipeline Parallelism
Divides the model into stages that run concurrently on different GPUs, improving training efficiency and throughput.
Mixed Precision Training
Uses FP16 precision to reduce memory usage and speed up training while maintaining model accuracy.
Highly Scalable
Designed to scale from a few GPUs to thousands, supporting multi-node distributed training seamlessly.
Customizable Model Architectures
Supports various transformer architectures like GPT, BERT, and their variants, allowing flexible experimentation.
Integration with NVIDIA Hardware
Optimized for NVIDIA GPUs and software stacks such as CUDA and NCCL for maximum performance.