Dell Technologies submitted several benchmark results for the latest MLCommons^TM Inference v3.0 benchmark suite. An objective was to provide information to help customers choose a favorable server and GPU combination for their workload. This blog reviews the Edge benchmark results and provides information about how to determine the best server and GPU configuration for different types of ML applications.

Results overview

For computer vision workloads, which are widely used in security systems, industrial applications, and even in self-driven cars, ResNet and RetinaNet results were submitted. ResNet is an image classification task and RetinaNet is an object detection task. The following figures show that for intensive processing, the NVIDIA A30 GPU, which is a double-wide card, provides the best performance with almost two times more images per second than the NVIDIA L4 GPU. However, the NVIDIA L4 GPU is a single-wide card that requires only 43 percent of the energy consumption of the NVIDIA A30 GPU, considering nominal Thermal Design Power (TDP) of each GPU. This low-energy consumption provides a great advantage for applications that need lower power consumption or in environments that are more challenging to cool. The NVIDIA L4 GPU is the replacement for the best-selling NVIDIA T4 GPU, and provides twice the performance with the same form factor. Therefore, we see that this card is the best option for most Edge AI workloads.

Conversely, the NVIDIA A2 GPU exhibits the most economical price (compared to the  NVIDIA A30 GPU's price), power consumption (TDP), and performance levels among all available options in the market. Therefore, if the application is compatible with this GPU, it has the potential to deliver the lowest total cost of ownership (TCO).

Figure 1: Performance comparison of NVIDIA A30, L4, T4, and A2 GPUs for the ResNet Offline benchmark

Figure 2: Performance comparison of NVIDIA A30, L4, T4, and A2 GPUs for the RetinaNet Offline benchmark

The 3D-UNet benchmark is the other computer vision image-related benchmark. It uses medical images for volumetric segmentation. We saw the same results for default accuracy and high accuracy. Moreover, the NVIDIA A30 GPU delivered significantly better performance over the NVIDIA L4 GPU. However, the same comparison between energy consumption, space, and cooling capacity discussed previously applies when considering which GPU to use for each application and use case.

Figure 3: Performance comparison of NVIDIA A30, L4, T4, and A2 GPUs for the 3D-UNet Offline benchmark 

Another important benchmark is for BERT, which is a Natural Language Processing model that performs tasks such as question answering and text summarization. We observed similar performance differences between the NVIDIA A30, L4, T4, and A2 GPUs. The higher the value, the better.

Figure 4: Performance comparison of NVIDIA A30, L4, T4, and A2 GPUs for the BERT Offline benchmark

MLPerf benchmarks also include latency results, which are the time that systems take to process requests. For some use cases, this processing time can be more critical than the number of requests that can be processed per second. For example, if it takes several seconds to respond to a conversational algorithm or an object detection query that needs a real-time response, this time can be particularly impactful on the experience of the user or application.

As shown in the following figures, the NVIDIA A30 and NVIDIA L4 GPUs have similar latency results. Depending on the workload, the results can vary due to which GPU provides the lowest latency. For customers planning to replace the NVIDIA T4 GPU or seeking a better response time for their applications, the NVIDIA L4 GPU is an excellent option. The NVIDIA A2 GPU can also be used for applications that require low latency because it performed better than the NVIDIA T4 GPU in single stream workloads. The lower the value, the better.

Figure 4: Latency comparison of NVIDIA A30, L4, T4, and A2 GPUs for the ResNet single-stream and multistream benchmark

Figure 5: Latency comparison of NVIDIA A30, L4, T4, and A2 GPUs for the RetinaNet single-stream and multistream benchmark and the BERT single-stream benchmark

Dell Technologies submitted to various benchmarks to help understand which configuration is the most environmentally friendly as the data center’s carbon footprint is a concern today. This concern is relevant because some edge locations have power and cooling limitations. Therefore, it is important to understand performance compared to power consumption.

The following figure affirms that the NVIDIA L4 GPU has equal or better performance per watt compared to the NVIDIA A2 GPU, even with higher power consumption. For Throughput and Perf/watt values, higher is better; for Power(watt) values, lower is better.

Figure 6: NVIDIA L4 and A2 GPU power consumption comparison

Conclusion

With measured workload benchmarks on MLPerf Inference 3.0, we can conclude that all NVIDIA GPUs tested for Edge workloads have characteristics that address several use cases. Customers must evaluate size, performance, latency, power consumption, and price. When choosing which GPU to use and depending on the requirements of the application, one of the evaluated GPUs will provide a better result for the final use case.

Another important conclusion is that the NVIDIA L4 GPU can be considered as an exceptional upgrade for customers and applications running on NVIDIA T4 GPUs. The migration to this new GPU can help consolidate the amount of equipment, reduce the power consumption, and reduce the TCO; one NVIDIA L4 GPU can provide twice the performance of the NVIDIA T4 GPU for some workloads.

Dell Technologies demonstrates on this benchmark the broad Dell portfolio that provides the infrastructure for any type of customer requirement.

The following blogs provide analyses of other MLPerf^TM benchmark results:

• Dell Servers Excel in MLPerf™ Inference 3.0 Performance
• Dell Technologies’ NVIDIA H100 SXM GPU submission to MLPerf™ Inference 3.0
• Empowering Enterprises with Generative AI: How Does MLPerf™ Help Support
• Comparison of Top Accelerators from Dell Technologies’ MLPerf™

References

For more information about Dell Power Edge servers, go to the following links:

• Dell’s PowerEdge XR7620 for Telecom/Edge Compute
• Dell’s PowerEdge XR5610 for Telecom/Edge Compute
• PowerEdge XR4520c Compute Sled specification sheet
• PowerEdge XE2420 Spec Sheet

For more information about NVIDIA GPUs, go to the following  links:

• NVIDIA L4 Datasheet
• NVIDIA A30 Datasheet
• NVIDIA A2 Datasheet
• NVIDIA T4 Datasheet

MLCommons^TM Inference v3.0 results presented in this document are based on following system IDs: 

Table 1: MLPerf^TM system IDs

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.