Training AI models on massive GPU clusters presents significant challenges for model builders. Because manual intervention becomes impractical as job scale increases, automation is critical to maintaining high GPU utilization and training productivity. An exceptional training experience requires resilient systems that provide low-latency error attribution and automatic fail over based on root…
]]>First introduced in 2019, NVIDIA Megatron-LM sparked a wave of innovation in the AI community, enabling researchers and developers to use the underpinnings of this open-source library to further large language model (LLM) advancements. Today, many of the most popular LLM developer frameworks have been inspired by and built using the Megatron-LM library, spurring a wave of foundation models and AI…
]]>Natural Language Processing (NLP) has seen rapid progress in recent years as computation at scale has become more available and datasets have become larger. At the same time, recent work has shown large language models to be effective few-shot learners, with high accuracy on many NLP datasets without additional finetuning. As a result, state-of-the-art NLP models have grown at an exponential rate…
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