Inference, the work of using AI in applications, is moving into mainstream uses, and it's running faster than ever.
NVIDIA GPUs won all tests of AI inference in data center and edge computing systems in the latest round of the industry's only consortium-based and peer-reviewed benchmarks.
NVIDIA A100 and T4 GPUs swept all data center inference tests. NVIDIA A100 Tensor Core GPUs extended the performance leadership we demonstrated in the first AI inference tests held last year by MLPerf, an industry benchmarking consortium formed in May 2018.
The A100, introduced in May, outperformed CPUs by up to 237x in data center inference, according to the MLPerf Inference 0.7 benchmarks. NVIDIA T4 small form factor, energy-efficient GPUs beat CPUs by up to 28x in the same tests.
To put this into perspective, a single NVIDIA DGX A100 system with eight A100 GPUs now provides the same performance as nearly 1,000 dual-socket CPU servers on some AI applications.
Leadership performance enables cost efficiency in taking AI from research to production. This round of benchmarks also saw increased participation, with 23 organizations submitting - up from 12 in the last round - and with NVIDIA partners using the NVIDIA AI platform to power more than 85 percent of the total submissions.
A100 GPUs, Jetson AGX Xavier Take Performance to the Edge
While A100 is taking AI inference performance to new heights, the benchmarks show that T4 remains a solid inference platform for mainstream enterprise, edge servers and cost-effective cloud instances. In addition, the NVIDIA Jetson AGX Xavier builds on its leadership position in power constrained SoC-based edge devices by supporting all new use cases.
Jetson AGX Xavier joined the A100 and T4 GPUs in leadership performance at the edge. The results also point to our vibrant, growing AI ecosystem, which submitted 1,029 results using NVIDIA solutions representing 85 percent of the total submissions in the data center and edge categories. The submissions demonstrated solid performance across systems from partners including Altos, Atos, Cisco, Dell EMC, Dividiti, Fujitsu, Gigabyte, Inspur, Lenovo, Nettrix and QCT.
Expanding Use Cases Bring AI to Daily Life Backed by broad support from industry and academia, MLPerf benchmarks continue to evolve to represent industry use cases. Organizations that support MLPerf include Arm, Baidu, Facebook, Google, Harvard, Intel, Lenovo, Microsoft, Stanford, the University of Toronto and NVIDIA.
The latest benchmarks introduced four new tests, underscoring the expanding landscape for AI. The suite now scores performance in natural language processing, medical imaging, recommendation systems and speech recognition as well as AI use cases in computer vision.
You need go no further than a search engine to see the impact of natural language processing on daily life.
The recent AI breakthroughs in natural language understanding are making a growing number of AI services like Bing more natural to interact with, delivering accurate and useful results, answers and recommendations in less than a second, said Rangan Majumder, vice president of search and artificial intelligence at Microsoft.
Industry-standard MLPerf benchmarks provide relevant performance data on widely used AI networks and help make informed AI platform buying decisions, he said.
AI Helps Saves Lives in the Pandemic The impact of AI in medical imaging is even more dramatic. For example, startup Caption Health uses AI to ease the job of taking echocardiograms, a capability that helped save lives in U.S. hospitals in the early days of the COVID-19 pandemic.
That's why thought leaders in healthcare AI view models like 3D U-Net, used in the latest MLPerf benchmarks, as key enablers.
We've worked closely with NVIDIA to bring innovations like 3D U-Net to the healthcare market, said Klaus Maier-Hein, head of medical image computing at DKFZ, the German Cancer Research Center.
Computer vision and imaging are at the core of AI research, driving scientific discovery and representing core components of medical care. And industry-standard MLPerf benchmarks provide relevant performance data that helps IT organizations and developers accelerate their specific projects and applications, he added.
Commercially, AI use cases like recommendation systems, also part of the latest MLPerf tests, are already making a big impact. Alibaba used recommendation systems last November to transact $38 billion in online sales on Singles Day, its biggest shopping day of the year.
Adoption of NVIDIA AI Inference Passes Tipping Point AI inference passed a major milestone this year.
NVIDIA GPUs delivered a total of more than 100 exaflops of AI inference performance in the public cloud over the last 12 months, overtaking inference on cloud CPUs for the first time. Total cloud AI Inference compute capacity on NVIDIA GPUs has been growing roughly tenfold every two years.
GPUs in major cloud services now account for more inference performance than CPUs. With the high performance, usability and availability of NVIDIA GPU computing, a growing set of companies across industries such as automotive, cloud, robotics, healthcare, retail, financial services and manufacturing now rely on NVIDIA GPUs for AI inference. They include American Express, BMW, Capital One, Dominos, Ford, GE Healthcare, Kroger, Microsoft, Samsung and Toyota.
Companies across key industry sectors use NVIDIA's AI platform for inference. Why AI Inference Is Hard Use cases for AI are clearly expanding, but AI inference is hard for many reasons.
New kinds of neural networks like generative adversarial networks are constantly being spawned for new use cases and the models are growing exponentially. The best language models for AI now encompass billions of parameters, and research in the field is still young.
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Inference, the work of using AI in applications, is moving into mainstream uses, and it's running faster than ever.
