Two months after their debut sweeping MLPerf inference benchmarks, NVIDIA H100 Tensor Core GPUs set world records across enterprise AI workloads in the industry group's latest tests of AI training. Together, the results show H100 is the best choice for users who demand utmost performance when creating and deploying advanced AI models. MLPerf is the industry standard for measuring AI performance. It's backed by a broad group that includes Amazon, Arm, Baidu, Google, Harvard University, Intel, Meta, Microsoft, Stanford University and the University of Toronto. In a related MLPerf benchmark also released today, NVIDIA A100 Tensor Core GPUs raised the bar they set last year in high performance computing (HPC).

As the size and complexity of large language models (LLMs) continue to grow, NVIDIA is today announcing updates to the NeMo Megatron framework that provide training speed-ups of up to 30%. These updates–which include two trailblazing techniques and a hyperparameter tool to optimize and scale training of LLMs on any number of GPUs–offer new capabilities to train and deploy models using the NVIDIA AI platform. BLOOM, the world's largest open-science, open-access multilingual language model, with 176 billion parameters, was recently trained on the NVIDIA AI platform, enabling text generation in 46 languages and 13 programming languages. The NVIDIA AI platform has also powered one of the most powerful transformer language models, with 530 billion parameters, Megatron-Turing NLG model (MT-NLG). LLMs are one of today's most important advanced technologies, involving up to trillions of parameters that learn from text.

In its debut in the industry MLPerf benchmarks, NVIDIA Orin, a low-power system-on-chip based on the NVIDIA Ampere architecture, set new records in AI inference, raising the bar in per-accelerator performance at the edge. Overall, NVIDIA with its partners continued to show the highest performance and broadest ecosystem for running all machine-learning workloads and scenarios in this fifth round of the industry metric for production AI. In edge AI, a pre-production version of our NVIDIA Orin led in five of six performance tests. It ran up to 5x faster than our previous generation Jetson AGX Xavier, while delivering an average of 2x better energy efficiency. NVIDIA Orin is available today in the NVIDIA Jetson AGX Orin developer kit for robotics and autonomous systems.

As the size and complexity of large language models (LLMs) continue to grow, NVIDIA is today announcing updates to the NeMo Megatron framework that provide training speed-ups of up to 30%. These updates–which include two trailblazing techniques and a hyperparameter tool to optimize and scale training of LLMs on any number of GPUs–offer new capabilities to train and deploy models using the NVIDIA AI platform. BLOOM, the world's largest open-science, open-access multilingual language model, with 176 billion parameters, was recently trained on the NVIDIA AI platform, enabling text generation in 46 languages and 13 programming languages. The NVIDIA AI platform has also powered one of the most powerful transformer language models, with 530 billion parameters, Megatron-Turing NLG model (MT-NLG). LLMs are one of today's most important advanced technologies, involving up to trillions of parameters that learn from text.