Azure Scales 530B Parameter GPT-3 Mannequin with NVIDIA NeMo Megatron | Azure Weblog and Updates


This put up was co-authored by Hugo Affaticati, Technical Program Supervisor, Microsoft Azure HPC + AI, and Jon Shelley, Principal TPM Supervisor, Microsoft Azure HPC + AI.

Pure language processing (NLP), automated speech recognition (ASR), and text-to-speech (TTS) functions have gotten more and more widespread in immediately’s world. Most corporations have leveraged these applied sciences to create chatbots for managing buyer questions and complaints, streamlining operations, and eradicating among the heavy price burden that comes with headcount. However what you could not understand is that they’re additionally getting used internally to cut back threat and establish fraudulent habits, scale back buyer complaints, improve automation, and analyze buyer sentiment. It’s prevalent in most locations, however particularly in industries similar to healthcare, finance, retail, and telecommunications.

NVIDIA not too long ago launched the newest model of the NVIDIA NeMo Megatron framework, which is now in open beta. This framework can be utilized to construct and deploy giant language fashions (LLMs) with pure language understanding (NLU).

Combining NVIDIA NeMo Megatron with our Azure AI infrastructure provides a robust platform that anybody can spin up in minutes with out having to incur the prices and burden of managing their very own on-premises infrastructure. And naturally, we now have taken our benchmarking of the brand new framework to a brand new degree, to actually present the ability of the Azure infrastructure.

Reaching new milestones with 530B parameters

We used Azure NDm A100 v4-series digital machines to run the GPT-3 mannequin’s new NVIDIA NeMo Megatron framework and take a look at the boundaries of this sequence. NDm A100 v4 digital machines are Azure’s flagship GPU choices for AI and deep studying powered by NVIDIA A100 80GB Tensor Core GPUs. These situations have essentially the most GPU reminiscence capability and bandwidth, backed by NVIDIA InfiniBand HDR connections to assist scaling up and out. Finally, we ran a 530B-parameter benchmark on 175 digital machines, leading to a coaching time per step of as little as 55.7 seconds (figure1). This benchmark measures the compute effectivity and the way it scales by measuring the time taken per step to coach the mannequin after regular state is reached, with a mini-batch dimension of 1. Such excellent velocity wouldn’t have been potential with out InfiniBand HDR offering wonderful communication between nodes with out elevated latency.

The graph shows Azure’s performance results on the GPT-3 530 billion-parameter model with NVIDIA NeMo Megatron. The Training time per step decreases almost linearly from 88.2 seconds to 55.8 seconds when the number of nodes increases from 105 to 175.
Determine 1: Coaching time per step on the 530B-parameter benchmark from 105 to 175 digital machines.

These outcomes spotlight an virtually linear velocity improve, guaranteeing higher efficiency for a better variety of nodes—paramount for heavy or time-sensitive workloads. As proven by these runs with billions of parameters, prospects can relaxation assured that Azure’s infrastructure can deal with even essentially the most troublesome and complicated workloads, on demand.

“Pace and scale are each key to growing giant language fashions, and the newest launch of the NVIDIA NeMo Megatron framework introduces new methods to ship 30 % sooner coaching for LLMs,” stated Paresh Kharya, senior director of accelerated computing at NVIDIA. “Microsoft’s testing with NeMo Megatron 530B additionally exhibits that Azure NDm A100 v4 situations powered by NVIDIA A100 Tensor Core GPUs and NVIDIA InfiniBand networking present a compelling choice for attaining linear coaching speedups at huge scale.”

Showcasing Azure AI capabilities—now and sooner or later

Azure’s dedication is to make AI and HPC accessible to everybody. It consists of, however isn’t restricted to, offering the very best AI infrastructure that scales from the smallest use circumstances to the heaviest workloads. As we proceed to innovate to construct the very best platform to your AI workloads, our promise to you is to make use of the newest benchmarks to check our AI capabilities. These outcomes assist drive our personal innovation and showcase that there isn’t any restrict to what you are able to do. For all of your AI computing wants, Azure has you lined.

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