Abstract

Dell Technologies recently submitted results to MLPerf^TM Inference v3.0 in the closed division. This blog highlights the NVIDIA H100 PCIe GPU and compares the results to the NVIDIA A100 PCIe GPU with the PCIe form factor held constant.

Introduction

MLPerf Inference v3.0 submission falls under the benchmarking pillar of the MLCommons^TM consortium with the objective to make fair comparisons across server configurations. Submissions that are made to the closed division warrant an equitable comparison of the systems. 

This blog highlights the closed division submissions Dell Technologies made with the NVIDIA A100 GPU using the PCIe (peripheral component interconnect express) form factor. The PCIe form factor is an interfacing standard for connecting various high-speed components in hardware such as a computer or a server. Servers include a certain number of PCIe slots in which to insert GPUs or other additional cards. Note that there are different physical configurations for the slots to indicate the number of lanes for data to travel to and from the PCIe card. The NVIDIA H100 GPU is truly the latest and greatest GPU with NVIDIA AI Enterprise included; it is a dual-slot air cooled PCIe generation 5.0 GPU. This GPU runs at a memory bandwidth speed of over 2,000 megabits per second and up to seven Multi-Instance GPUs at 10 gigabytes each. The NVIDIA A100 80 GB GPU is a dual-slot PCIe generation 4.0 GPU that runs at a memory bandwidth speed of over 2,000 megabits per second.

NVIDIA H100 PCIe GPU and NVIDIA A100 PCIe GPU comparison

In addition to making a submission with the NVIDIA A100 GPU, Dell Technologies made a submission with the NVIDIA H100 GPU. To make a fair comparison, the systems were identical and the PCIe form factor was held constant.

Table 1: Software stack of submissions made on NVIDIA A100 PCIe and NVIDIA H100 PCIe GPUs for MLPerf Inference v3.0 on the Dell PowerEdge R750xa server

In the following figure, the per card numbers are normalized over the NVIDIA A100 GPU results to show a readable comparison of the GPUs on the same system. Across object detection, medical image segmentation, and speech to text and natural language processing, the latest NVIDIA H100 GPU outperforms its predecessor in all categories. Note the outstanding performance of the Dell PowerEdge R750xa server with NVIDIA H100 GPUs with the BERT benchmark in the high accuracy mode. With the advancements in generative artificial intelligence, the Dell PowerEdge R750xa server is a versatile, reliable, and high performing platform.

Figure 1: Normalized per GPU comparison of NVIDIA A100 and NVIDIA H100 GPUs on the Dell PowerEdge R750xa server 

The following figures show absolute numbers for a comparison of the NVIDIA H100 and NVIDIA A100 GPUs.

Figure 2: Per GPU comparison of NVIDIA A100 and NVIDIA H100 GPUs for RetinaNet on the PowerEdge R750xa server

Figure 3: Per GPU comparison of NVIDIA A100 and NVIDIA H100 GPUs for 3D-Unet on the PowerEdge R750xa server 

Figure 4: Per GPU comparison of NVIDIA A100 and NVIDIA H100 GPUs for RNNT on the PowerEdge R750xa server

Figure 5: Per GPU comparison of NVIDIA A100 and NVIDIA H100 GPUs for BERT on the PowerEdge R750xa server 

These results can be found on the MLCommons website.

Submissions made with the NVIDIA A100 PCIe GPU

In this round of submissions, Dell Technologies submitted results on the PowerEdge R750xa server packaged with four NVIDIA A100 80 GB PCIe GPUs. In previous rounds, the PowerEdge R750xa server showed outstanding performance across all the benchmarks. For a deeper dive of a previous round's submission, check out our blog from MLPerf Inference v2.0. From the previous round of MLPerf Inference v2.1 submissions, Dell Technologies submitted results on an identical system. However, across the two rounds of submissions, the main difference is the upgrades in the software stack, as described in the following table:

Table 2: Software stack for submissions made on the NVIDIA A100 PCIe GPU in MLPerf Inference v3.0 and v2.1

Comparison of PowerEdge R750xa NVIDIA A100 results from Inference v3.0 and v2.1

Object detection

The RetinaNet benchmark falls under the object detection category and uses the OpenImages dataset. The results from Inference v3.0 show a less than 0.05 percent difference in the Server scenario and a 21.53 percent difference in the Offline scenario. A potential reason for this result might be NVIDIA’s optimizations, as outlined in their technical blog.

Figure 6: RetinaNet Server and Offline results on the PowerEdge R750xa server from Inference v3.0 and Inference v2.1 

Medical image segmentation

The 3D-Unet benchmark performs the KiTS 2019 kidney tumor segmentation task. Across the two rounds of submission, the PowerEdge R750xa server performed consistently well with a 0.3 percent difference in both the default and high accuracy modes.

Figure 7: 3D-UNet Offline results on the PowerEdge R750xa server from Inference v3.0 and v2.1

Speech to text

The Recurrent Neural Network Transducers (RNNT) model falls under the speech recognition category. This benchmark accepts raw audio samples and produces the corresponding character transcription. In the Server scenario, the results are within a 2.25 percent difference and 0.41 percent difference in the Offline scenario.

Figure 8: RNNT Server and Offline results on the Dell PowerEdge R750xa server from Inference v3.0 and v2.1 

Natural language processing

Bidirectional Encoder Representation from Transformers (BERT) is a state-of-the-art language representational model for Natural Language Processing applications. This benchmark performs the SQuAD question answering task. The BERT benchmark consists of default and high accuracy modes for the Offline and Server scenarios. For the Server scenarios, the default mode results are within a 1.69 percent range and 3.12 percent range for the high accuracy mode. For the Offline scenarios, a similar behavior is noticeable in which the default mode results are within a 0.86 percent range and 3.65 percent range in the high accuracy mode.

Figure 9: BERT Server and Offline results on the PowerEdge R750xa server from Inference v3.0 and v2.1 

Conclusion

Across the various rounds of submissions to the MLPerf Inference benchmark suite, the PowerEdge R750xa server has been a consistent top performer for any machine learning tasks ranging from object detection, medical image segmentation, speech to text and natural language processing. The PowerEdge R750xa server continues to be an excellent server choice for machine learning inference workloads. Customers can take advantage of the diverse results submitted on the Dell PowerEdge R750xa server with the NVIDIA H100 GPU to make an informed decision for their specific solution needs.

MLCommons has released the latest version (version 3.0) of MLPerf Inference results. Dell Technologies has been an MLCommons member and has been making submissions since the inception of the MLPerf Inference benchmark. Our latest results exhibit stellar performance from our servers and continue to shine in all areas of the benchmark including image classification, object detection, natural language processing, speech recognition, recommender system, and medical image segmentation. We encourage you to see our previous whitepaper about Inference v2.1, which introduces the MLCommons inference benchmark. AI and the most recent awareness of Generative AI, with application examples such as ChatGPT, have led to an increase in understanding about the performance objectives needed to enable customers with faster time-to-model and results. The latest results reflect the continued innovation that Dell Technologies brings to help customers achieve those performance objectives and speed up their initiatives to assess and support workloads including Generative AI in their enterprise.

What’s new with Inference 3.0?

New features for Inference 3.0 include:

• The inference benchmark rules did not make any significant changes. However, our submission has been expanded with the new generation of Dell PowerEdge servers:
  • Our submission includes the new PowerEdge XE9680, XR7620, and XR5610 servers.
  • Our results address new accelerators from our partners such as NVIDIA and Qualcomm.
• We submitted virtualized results with VMware running on NVIDIA  AI Enterprise software and NVIDIA accelerators.
• Besides accelerator-based numbers, we submitted Intel-based CPU-only results.

Overview of results

Dell Technologies submitted 255 results across 27 different systems. The most outstanding results were generated from PowerEdge R750xa and XE9680 servers with the new H100 PCIe and SXM accelerators, respectively, as well as PowerEdge XR5610 and XR7620 servers with the L4 cards. Our overall NVIDIA-based results include the following accelerators:

• (New) Eight-way NVIDIA H100 Tensor Core GPU (SXM)
• (New) Four-way NVIDIA H100 Tensor Core GPU (PCIe)
• (New) Eight-way NVIDIA A100 Tensor Core GPU (SXM)
•  Four-way NVIDIA A100 Tensor Core GPU (PCIe)
• NVIDIA A30 Tensor Core GPU
• (New) NVIDIA L4 Tensor Core GPU
• NVIDIA A2 GPU
• NVIDIA T4  GPU

We ran these accelerators on different PowerEdge XE9680, R750xa, R7525, XE8545, XR7620, XR5610 and other server configurations.

This variety of results across different servers, accelerators, and deep learning use cases allows customers to use them as datapoints to make purchasing decisions and set performance expectations.

Interesting Dell datapoints

The most interesting datapoints include:

• Among 21 submitters, Dell Technologies was one of the few companies that submitted results for all closed scenarios including data center, data center power, Edge, and Edge power.
• The PowerEdge XE9680 server procures the highest performance titles with RetinaNet Server and Offline, RNN-T Server, and BERT 99 Server benchmarks. It procures number 2 performance with Resnet Server and Offline, 3D-UNet Offline and 3D-UNet Offline 99.9, BERT 99 Offline, BERT 99.9 Server, and RNN-T Offline benchmarks.
• The PowerEdge XR5610 server procured highest system performance per watt with the NVIDIA L4 accelerator on the ResNet Single Stream, Resnet Multi Stream, RetinaNet Single Stream, RetinaNet Offline, RetinaNet Multi Stream, 3D-UNet 99, 3D-UNet 99.9 Offline, RNN-T Offline, Single Stream, BERT 99 Offline, BERT-99 Single Stream benchmarks.
• Our results included not only various systems, but also exceeded performance gains compared to the last round because of the newer generation of hardware acceleration from the newer server and accelerator.
• The Bert 99.9 benchmark was implemented with FP8 the first time. Because of the accuracy requirement, in the past, the Bert 99.9 benchmark was implemented with FP16 while all other models ran under INT8.

In the following figure, the BERT 99.9 v3.0 Offline scenario renders 843 percent more improvement compared to Inference v2.1. This result is due to the new PowerEdge XE9680 server, which is an eight–way NVIDIA H100 SXM system, compared to the previous PowerEdge XE8545 four-way NVIDIA A100 SXM. Also, the NVIDIA H100 GPU features a Transformer Engine  with FP8 precision that speeds up results dramatically.

^{* MLPerf ID 2.1-0014 and MLPerf ID 3.0-0013}

Figure 1: Performance gains from Inference v2.1 to Inference v3.0 due to the new system

Results at a glance

The following figure shows the system performance for Offline and Server scenarios. These results provide an overview as upcoming blogs will provide details about these results. High accuracy versions of the benchmark are included for DLRM and 3D-UNet as high accuracy version results are identical to the default version. For the BERT benchmark, both default and high accuracy versions are included as they are different.

Figure 2: System throughput for data center suite-submitted systems

The following figure shows the Single Stream and Multi Stream scenario latency for the ResNet, RetinaNet, 3D-UNet, RNN-T, and BERT-99 benchmarks. The lower the latency, the better the results.

Fig 3: Latency of the systems for different benchmark

Edge benchmark results include Single Stream, Multi Stream, and Offline scenarios. The following figure shows the offline scenario performance.

Figure 4: Offline Scenario system throughput for Edge suite  

The preceding figures show that PowerEdge servers delivered excellent performance across various benchmarks and scenarios.

Conclusion

We have provided MLCommons-compliant submissions to the Inference 3.0 benchmark across various benchmarks and suites. These results indicate that with the newer generations of servers, such as the PowerEdge XE9680 server, and newer accelerators, such as the NVIDIA H100 GPU, customers can derive higher performance from their data center and edge deployments. Upgrading to newer hardware can yield between 304 and 843 percent improvement across all the benchmarks such as image classification, object detection, natural language processing, speech recognition, recommender systems, and medical image segmentation involved in the MLPerf inference benchmark.  From our submissions  for new servers such as the PowerEdge XR5610 and XR7620 servers with the NVIDIA L4 GPU, we see exceptional results. These results show that the new PowerEdge servers are an excellent edge platform choice. Furthermore, our variety of submissions to the benchmark can serve as a baseline to enable different performance expectations and cater to purchasing decisions. With these results, Dell Technologies can help fuel enterprises’ AI transformation, including Generative AI adoption, and deployment precisely and efficiently.

MLCommons Results

•  https://mlcommons.org/en/inference-datacenter-30/
• https://mlcommons.org/en/inference-edge-30/