This post shows you how to configure training jobs on Amazon SageMaker AI to get the most out of Blackwell’s architecture on AWS. You learn how to select batch sizes and sequence lengths that take advantage of Blackwell’s expanded memory, choose the right precision format for your model size (1B to 64B parameters), and apply activation checkpointing strategically. By the end, you have a practical framework for tuning your training configuration and launching distributed training jobs on P6-B200 instances.

Properly configured Blackwell training jobs can process larger batch sizes without aggressive sharding, reducing communication overhead and improving throughput. Longer sequence lengths become viable for long-range dependency tasks. With the right precision format, models that previously required multi-node setups can run on a single 8-GPU node, which means faster iteration cycles, less networking overhead, and lower infrastructure costs.

Before you configure your training job, it helps to understand what makes Blackwell different from previous GPU generations. Blackwell’s dual-chip architecture and fifth-generation Tensor Cores deliver measurable gains for multi-GPU training out of the box. The NVLink 5 interconnect provides up to 1.8 TB/s of bidirectional GPU-to-GPU bandwidth, while B200’s larger HBM capacity and higher memory bandwidth help reduce memory pressure for large batches, long sequences, and distributed training workloads.

The examples in this post use single-node 8-GPU training with transformer models ranging from 1B to 64B parameters. The training configuration uses PyTorch Fully Sharded Data Parallel (FSDP), a distributed training technique that shards model parameters, gradients, and optimizer states across GPUs to train models larger than single-GPU memory. The results cover multiple configurations with varying batch sizes, sequence lengths, and precision formats to show when different approaches deliver the optimal results.

Blackwell’s expanded memory (180 GB on B200, 268 GB on B300) gives you room to optimize in three areas: larger batch sizes, simplified model sharding, and longer sequence lengths.