Tackling AI Inference workloads on Azure’s NC A100 v4 virtual machines with time to spare

By Hugo Affaticati

October 26, 2022

Introduction

The NC A100 v4-series virtual machines (VMs) on Azure offer great flexibility for a wide range of workloads. Powered by NVIDIA A100 80GB PCIe Tensor Core GPUs and 3rd generation AMD EPYC 7V13 (Milan) processors, these instances are well-suited for autonomous vehicle training, oil and gas reservoir simulation, video processing, AI/ML inference-powered web services, and much more. They are available in different sizes and configurations to support various computational needs, ranging from one to four NVIDIA GPUs per VM, with the ability to separate each NVIDIA A100 GPU into as many as seven isolated GPU partitions with NVIDIA Multi-Instance GPU (MIG) technology. You can find more details about the product on Microsoft Docs product page.

In this document, we share compelling AI results from MLPerf™ benchmarks by MLCommons® [1] that showcase the adaptability of the NC A100 v4-series, along with the best practices and configuration details you need to be able to replicate them. And as a result, not only do we show that the latest NC A100 v4-series VM performances are applicable to a large range of workloads (from low- to mid-size), but also that they are competitive against similar on-premises offerings and the most cost competitive offering for small workloads.

Key Performance Results

NC A100 v4 adapts from low to mid-size AI workloads

One of the outstanding benefits of the NC A100 v4-series is the capacity to run jobs on the full GPUs or to run jobs in parallel on 2, 3, or 7 partitions of the GPU. We compared the inference performance obtained using a single MIG instance (1/7th of an NVIDIA A100 GPU) of the NC96ads A100 v4 VM to those obtained with one GPU of the NC64as_t4_v3 VM. The NCas_T4_v3-series is powered by NVIDIA T4 Tensor Core GPUs and AMD EPYC 7V12 processor cores and continues to be a benchmark product for entry-level training and inference. Both configurations (the T4-based and the 1/7th A100-based) show almost equal performance, using the MLPerfTM Inference* v2.1 benchmarks. The 1/7th of the A100 GPU even shows a 25% increase in sequences per second for speech recognition workloads, as shown in figure 1. For mid-size workloads, NC A100 v4-series showcases a significant boost in performance with four NVIDIA A100 GPUs. Because the creation of MIG instances is reversible, customers can provision the right-sized GPU acceleration for small to mid-size workloads and always meet their evolving needs with a single virtual machine.

 

thumbnail image 1 of blog post titled Tackling AI Inference workloads on Azure’s NC A100 v4 virtual machines with time to spare
Figure 1 – Speedup factor with 1/7th of an NVIDIA A100 GPU on NCads A100 v4-series compared to a T4 accelerator on NCas_T4_v3-series with MLPerf Inference v2.1 for Offline scenario.

NC A100 v4 is competitive with on-premises performance

Using the MLPerf™ benchmarks, we compared the performance of our on-demand MIG instances on NC A100 v4 VMs to on-premises offerings. Our unverified results for MLPerf™ Inference v2.1 [2] are in line with the submission from the on-premises category of closed division results for MIG instances on the NVIDIA A100 PCIe GPU for MLPerf™ Inference v1.1. This showcases Azure’s uncompromising commitment to enabling customers to use the best available “on-demand” cloud resources to address their workload needs, without sacrifices or approximations.

NC A100 v4 is cost competitive

To understand if it makes sense from a cost perspective to run a MIG A100 instance that is 1/7th of the total GPU vs. a single NCas T4 v3 GPU, we calculated the number of sequences each could compute with a single dollar. For these calculations, we used the “pay as you go” price for machines available in region East US 2 and the throughput in samples/s for MLPerf Inference v2.1 benchmarks. For the inference models run, we see a minimum of 2x improvement in performance per dollar, as shown in figure 2. The speech recognition type of workloads (RNN-T benchmark), however, shows a staggering 2.9x increase in the number of sequences per dollar by using seven MIG A100 instances. Thus, if the workload requires three or more NCas T4 v3 VMs, it is more cost-effective to deploy one NC A100 v4 VM and enable MIG instances (it is not possible to deploy only one MIG instance that is 1/7th of the GPU). Note other GPU maximum MIG slice configurations are possible besides 7, including 2, or 3 total MIG instance configurations.

thumbnail image 2 of blog post titled Tackling AI Inference workloads on Azure’s NC A100 v4 virtual machines with time to spare
Figure 2 – Number of sequences processed per dollar spent on Azure with seven instances of NC A100 v4-series (MIG) and seven accelerators of NCas_T4_v3-series across three benchmarks for MLPerf Inference v2.1.

Highlights of Performance Results

The tables below showcase performance results of the NC64as_T4_v3 (4 GPUs) and NC96ads A100 v4 (1/7th of a GPU) VMs for offline inference scenario with MLPerf™ Inference v2.1.

System NC64as T4 v3 [1]
Benchmark BERT RNN-T ResNet-50 3D-UNet
Score (samples/s) 1,688 6,059 24,321 1.9

* The results above were not verified by MLCommons Association

System  1 of 7 instances (MIG) on NC96ads A100 v4 [2] 
Model 

 

BERT 

(default) 

BERT 

(high accuracy) 

RNN-T  ResNet-50  3D-UNet (default)  3D-UNet 

(high accuracy) 

Score (samples/s)  491  245  1,901  5,406  0.5  0.5 

* The results above were not verified by MLCommons Association

Conclusion

The NC A100 v4-series offers great flexibility through MIG technology to handle different sizes of workload, from small to medium. While we compared the performance of a single MIG instance (1/7th of an NVIDIA A100 GPU) with a full NVIDIA T4 GPU, one can partition the A100 GPU in two or three instances for more compute capabilities, or even keep the A100 GPU at its full capacity. The results speak for themselves as we compared one A100 MIG instance for a 7-MIG configuration to the gold standard of small AI workload GPUs, the T4: deploying a NC A100 v4 VM is more cost-performant if the workload requires three or more T4 GPUs. You can reproduce these impressive results using the guidelines below.

Recreate the Results in Azure

To get started with NC A100 v4-series, please visit the following links:

[1] MLCommons® is an open engineering consortium of AI leaders from academia, research labs, and industry where the mission is to “build fair and useful benchmarks” that provide unbiased evaluations of training and inference performance for hardware, software, and services—all conducted under prescribed conditions. MLPerf™ tests are transparent and objective real-world compute-intensive AI workloads, so technology decision makers can rely on the results to make informed buying decisions.

[2] Result not verified by MLCommons Association. The MLPerf™ name and logo are trademarks of MLCommons Association in the United States and other countries. All rights reserved. Unauthorized use is strictly prohibited. See www.mlcommons.org for more information. The results were obtained on the NC96ads A100 v4 virtual machine using Ubuntu HPC 20.04 image, cuda 12.0, NVIDIA driver version 525.60.13, and MLPerf Inference v2.1 libraries & datasets. One can reproduce the results by following this guide.

#MakeAIYourReality
#AzureHPCAI
#NVIDIAonAzure

Return to Solution Channel Homepage
Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, code-named Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from its predecessors, including the red-hot H100 and A100 GPUs. Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. While Nvidia may not spring to mind when thinking of the quant Read more…

2024 Winter Classic: Meet the HPE Mentors

March 18, 2024

The latest installment of the 2024 Winter Classic Studio Update Show features our interview with the HPE mentor team who introduced our student teams to the joys (and potential sorrows) of the HPL (LINPACK) and accompany Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the field was normalized for boys in 1969 when the Apollo 11 missi Read more…

Apple Buys DarwinAI Deepening its AI Push According to Report

March 14, 2024

Apple has purchased Canadian AI startup DarwinAI according to a Bloomberg report today. Apparently the deal was done early this year but still hasn’t been publicly announced according to the report. Apple is preparing Read more…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimization algorithms to iteratively refine their parameters until Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, code-named Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the fi Read more…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimizat Read more…

PASQAL Issues Roadmap to 10,000 Qubits in 2026 and Fault Tolerance in 2028

March 13, 2024

Paris-based PASQAL, a developer of neutral atom-based quantum computers, yesterday issued a roadmap for delivering systems with 10,000 physical qubits in 2026 a Read more…

India Is an AI Powerhouse Waiting to Happen, but Challenges Await

March 12, 2024

The Indian government is pushing full speed ahead to make the country an attractive technology base, especially in the hot fields of AI and semiconductors, but Read more…

Charles Tahan Exits National Quantum Coordination Office

March 12, 2024

(March 1, 2024) My first official day at the White House Office of Science and Technology Policy (OSTP) was June 15, 2020, during the depths of the COVID-19 loc Read more…

AI Bias In the Spotlight On International Women’s Day

March 11, 2024

What impact does AI bias have on women and girls? What can people do to increase female participation in the AI field? These are some of the questions the tech Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Analyst Panel Says Take the Quantum Computing Plunge Now…

November 27, 2023

Should you start exploring quantum computing? Yes, said a panel of analysts convened at Tabor Communications HPC and AI on Wall Street conference earlier this y Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Training of 1-Trillion Parameter Scientific AI Begins

November 13, 2023

A US national lab has started training a massive AI brain that could ultimately become the must-have computing resource for scientific researchers. Argonne N Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire