NVIDIA NeMo Framework
Huge language models train on enormous datasets distributed over thousands of NVIDIA GPUs. To better serve their clients, AWS and NVIDIA want to use the knowledge gained from their partnership in products and services such as Amazon Titan and NVIDIA NeMo. For businesses looking to implement generative AI, that might mean a lot of obstacles. NVIDIA NeMo, a platform for creating, modifying, and operating LLMs, aids in resolving these issues. Over the last several months, a group of seasoned scientists and engineers working at Amazon Web Services have been utilizing NVIDIA NeMo to build foundation models for Amazon Bedrock, an AI generative service. These models will form the basis of Amazon Titan.
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Using NeMo’s parallelism approaches, large-scale LLM training becomes efficient. The team was able to speed up training by distributing the LLM over many GPUs when they used it in conjunction with the Elastic Fabric Adapter from Amazon Web Services. With EFA’s UltraCluster Networking architecture, AWS users may connect over 10,000 GPUs directly, skipping the OS and CPU altogether with NVIDIA GPUDirect.
Together, they made it feasible for the AWS scientists to produce high-quality models, which would have been impossible to do at scale with data parallelism alone. Among AWS’s breakthroughs is the ability to transmit data efficiently from Amazon S3 to the GPU cluster. With NeMo’s foundation in well-known libraries like PyTorch Lightning, which standardizes components of LLM training pipelines.
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