Training a frontier model is highly compute-intensive, requiring a distributed system of hundreds, or thousands, of accelerated instances running for several weeks or months to complete a single job. For example, pre-training the Llama 3 70B model with 15 trillion training tokens took 6.5 million H100 GPU hours. On 256Continue Reading
Foundation model (FM) training and inference has led to a significant increase in computational needs across the industry. These models require massive amounts of accelerated compute to train and operate effectively, pushing the boundaries of traditional computing infrastructure. They require efficient systems for distributing workloads across multiple GPU accelerated servers,Continue Reading
This post is cowritten with Abdullahi Olaoye, Akshit Arora and Eliuth Triana Isaza at NVIDIA. As enterprises continue to push the boundaries of generative AI, scalable and efficient model training frameworks are essential. The NVIDIA NeMo Framework provides a robust, end-to-end solution for developing, customizing, and deploying large-scale AI models,Continue Reading
The rise of generative AI has significantly increased the complexity of building, training, and deploying machine learning (ML) models. It now demands deep expertise, access to vast datasets, and the management of extensive compute clusters. Customers also face the challenges of writing specialized code for distributed training, continuously optimizing models,Continue Reading
Increasingly, organizations across industries are turning to generative AI foundation models (FMs) to enhance their applications. To achieve optimal performance for specific use cases, customers are adopting and adapting these FMs to their unique domain requirements. This need for customization has become even more pronounced with the emergence of newContinue Reading
At AWS re:Invent 2024, we launched a new innovation in Amazon SageMaker HyperPod on Amazon Elastic Kubernetes Service (Amazon EKS) that enables you to run generative AI development tasks on shared accelerated compute resources efficiently and reduce costs by up to 40%. Administrators can use SageMaker HyperPod task governance toContinue Reading
Training large language models (LLMs) models has become a significant expense for businesses. For many use cases, companies are looking to use LLM foundation models (FM) with their domain-specific data. However, companies are discovering that performing full fine tuning for these models with their data isn’t cost effective. To reduce costsContinue Reading
This post is co-written with Marta Cavalleri and Giovanni Germani from Fastweb, and Claudia Sacco and Andrea Policarpi from BIP xTech. AI’s transformative impact extends throughout the modern business landscape, with telecommunications emerging as a key area of innovation. Fastweb, one of Italy’s leading telecommunications operators, recognized the immense potentialContinue Reading
Amazon SageMaker HyperPod is designed to support large-scale machine learning (ML) operations, providing a robust environment for training foundation models (FMs) over extended periods. Multiple users — such as ML researchers, software engineers, data scientists, and cluster administrators — can work concurrently on the same cluster, each managing their ownContinue Reading
Today, organizations are constantly seeking ways to use advanced large language models (LLMs) for their specific needs. These organizations are engaging in both pre-training and fine-tuning massive LLMs, with parameter counts in the billions. This process aims to enhance model efficacy for a wide array of applications across diverse sectors,Continue Reading
