Microsoft Azure is committed to providing its customers with industry-leading real-world AI capabilities. In December 2021, Microsoft Azure debuted its leadership performance with the MLPerf training v1.1 results. Azure debuted at number one among cloud providers and number two overall at scale among all submitters. Azure’s supercomputer's building blocks were used to generate the results in our v2.0 submissions for the MLPerf inferencing results published on April 6, 2022.
These industry-leading results are driven by Microsoft’s publicly available supercomputing capabilities designed for real-world AI inferencing workloads. Microsoft enables customers of all scales to deploy powerful AI solutions, whether at a focused local scale or at the scale of the largest supercomputers in the world.
Microsoft Azure’s publicly available AI inferencing capabilities are led by the NDm A100 v4, ND A100 v4, and NC A100 v4 virtual machines (VMs) that are powered by NVIDIA A100 SXM and PCIe Tensor Core graphics processing units (GPUs). These results showcase Azure’s commitment to making AI inferencing available to all in the most accessible way—while raising the bar for AI inferencing in Azure.
In our quest to continually provide the best technology for our customers, Azure has recently announced the preview for the NC A100 v4. With this introduction of the NC A100 v4 series, we have provided our customers with three different VM sizes ranging from one to four GPUs. From our benchmarking, we have seen more than two times performance over the previous generation. Azure’s customers can get access to these new systems today by signing up for the preview program.
Some highlights for this round of MLPerf inferencing submissions can be seen in the following tables.
Highlights from the results
ND96amsr A100 v4 powered by NVIDIA A100 80G SXM Tensor Core GPU
Benchmark | Samples/second | Queries/second | Scenarios |
bert-99 | 27,500 plus | ~22,500 plus | Offline and server |
resnet | 300,000 plus | ~200,000 plus | Offline and server |
3d-unet | 24.87 | Offline |
Benchmark | Samples/second | Queries/second | Scenarios |
bert-99 | ~6,300 | ~5,300 | Offline and server |
resnet | 144,000 | ~119,600 | Offline and server |
3d-unet | 11.7 | Offline |
0 comments:
Post a Comment