MLPerf™ Inference v3.1 Edge Workloads Powered by Dell PowerEdge Servers
Tue, 19 Sep 2023 12:07:00 -0000
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Abstract
Dell Technologies recently submitted results to the MLPerf Inference v3.1 benchmark suite. This blog examines the results on the Dell PowerEdge XR4520c, PowerEdge XR7620, and PowerEdge XR5610 servers with the NVIDIA L4 GPU.
MLPerf Inference background
The MLPerf Inference benchmarking suite is a comprehensive framework designed to fairly evaluate the performance of a wide range of machine learning inference tasks on various hardware and software configurations. The MLCommonsTM community aims to provide a standardized set of deep learning workloads with which to work and as fair measuring and auditing methodologies. The MLPerf Inference submission results serve as valuable information for researchers, customers, and partners to make informed decisions about inference capabilities on various edge and data center systems.
The MLPerf Inference edge suite includes three scenarios:
- Single-stream—This scenario’s performance metric is 90 percent latency. A common use case is the Siri voice assistant on iOS products on which Siri’s engine waits until the query has been asked and then returns results.
- Multi-stream—This scenario has a higher performance metric with a 99 percent latency. An example use case is self-driving cars. Self-driving cars use multiple cameras and lidar inputs to real-time driving decisions that have a direct impact on what happens on the road.
- Offline—This scenario is measured by throughput. An example of Offline processing on the edge is a phone sharing an album suggestion that is based on a recent set of photos and videos from a particular event.
Edge computing
In traditional cloud computing at the data center, data from phones, tablets, sensors, and machines are sent to physically distant data centers to be processed. The location of where the data has been gathered and where it is processed are separate. The concept of edge computing shifts this methodology by processing data on the device itself or on local compute resources that are available nearby. The available compute resources nearby are known as the “devices on the edge.” Edge computing is prevalent in several industries such as self-driving cars, retail analytics, truck fleet management, smart grid energy distribution, healthcare, and manufacturing.
Edge computing complements traditional cloud computing by reducing processing speed in terms of lowering latency, improving efficiency, enhancing security, and enabling higher reliability. By processing data on the edge, the load on central data centers is eased as is the time to receive a response for any type of inference queries. With the offloading of computation in data centers, network congestion for cloud users becomes less of a concern. Also, because sensitive data is processed at the edge and is not exposed to threats across a wider network, the risk of sensitive data being compromised is less. Furthermore, if connectivity to the cloud is disrupted and is intermittent, edge computing can enable systems to continue functioning. With several devices on the edge acting as computational minidata centers, the problem of a single point of failure is mitigated and additional scalability becomes easily achievable.
Dell PowerEdge system and GPU overview
Dell PowerEdge XR4520c server
For projects that need a robust and adaptable server to handle demanding AI workloads on the edge, the PowerEdge XR4520c server is an excellent option. Dell Technologies designed the PowerEdge XR4520c server with reliability to withstand challenging edge environments. The PowerEdge XR4520c server delivers the power and compute required for real-time analytics on the edge with Intel Xeon Scalable processors. The edge-optimized design decisions include a rugged exterior and an extended temperature range to operate in remote locations and industrial environments. Also, the compact form factor and space-efficient design enable deployment on the edge. Like all Dell PowerEdge products, this server comes with world class Dell support and Dell’s (Integrated Dell Remote Access Controller (iDRAC) for remote management. For additional information about the technical specifications of the PowerEdge XR4520c server, see to the specification sheet.
Figure 1: Front view of the Dell PowerEdge XR4520c server
Figure 2: Top view of the Dell PowerEdge XR4520c server
Dell PowerEdge XR7620 server
The PowerEdge XR7620 server is top-of-the-line for deep learning in the edge. Powered with the latest Intel Xeon Scalable processors, the reduced training time and additional number of inferences is remarkable on the PowerEdge XR7620 server. Dell Technologies has designed this as a half-width server for rugged environments with a dust and particle filter and extended temperature range from –5C to 55C (23 F to 131 F). Furthermore, Dell’s comprehensive security and data protection features include data encryption and zero-trust logic for the protection of sensitive data. For additional information about the technical specifications of the PowerEdge XR7620 server, see the specification sheet.
Figure 3: Front view of the Dell PowerEdge XR7620 server
Figure 4: Rear view of the Dell PowerEdge XR7620 server
Dell PowerEdge XR5610 server
The Dell PowerEdge XR5610 server is an excellent option for AI workloads on the edge. This all-pupose, rugged single-socket server is a versatile edge server that has been built for telecom, defense, retail and other demanding edge environments. As shown in the following figures, the short chassis can fit in space-constrained environments and is also a formidable option when considering power efficiency. This server is driven by Intel Xeon Scalable processors and is boosted with NVIDIA GPUs as well as high-speed NVIDIA NVLink interconnects. For additional information about the technical specifications of the PowerEdge XR5610 server, see the specification sheet.
Figure 5: Front view of the Dell PowerEdge XR5610 server
Figure 6: Top view of the Dell PowerEdge XR5610 server
NVIDIA L4 GPU
The NVIDIA L4 GPU is an excellent strategic option for the edge as it consumes less energy and space but delivers exceptional performance. The NVIDIA L4 GPU is based on the Ada Lovelace architecture and delivers extraordinary performance for video, AI, graphics, and virtualization. The NVIDIA L4 GPU comes with NVIDIA’s cutting-edge AI software stack including CUDA, cuDNN, and support for several deep learning frameworks like Tensorflow and PyTorch.
Systems Under Test
The following table lists the Systems Under Test (SUT) that are described in this blog.
Table 1: MLPerf Inference v3.1 system configuration of the Dell PowerEdge XR7620 and the PowerEdge XR4520c servers
Platform | Dell PowerEdge XR7620 (1x L4, TensorRT) | Dell PowerEdge XR4520c (1x L4, TensorRT) |
MLPerf system ID | XR7620_L4x1_TRT | XR4520c_L4x1_TRT |
Operating system | CentOS 8 | Ubuntu 22.04 |
CPU | Dual Intel Xeon Gold 6448Y CPU @ 2.10 GHz | Single Intel Xeon D-2776NT CPU @ 2.10 |
Memory | 256 GB | 128 GB |
GPU | NVIDIA L4 | |
GPU count | 1 | |
Software stack | TensorRT 9.0.0 CUDA 12.2 cuDNN 8.8.0 Driver 535.54.03 DALI 1.28.0 | TensorRT 9.0.0 CUDA 12.2 cuDNN 8.9.2 Driver 525.105.17 DALI 1.28.0
|
Performance from Inference v3.1
The following figure compares the Dell PowerEdge XR4520c and PowerEdge XR7620 servers across the ResNet50, RetinaNet, RNNT and BERT 99 Single-stream, Multi-stream, and Offline benchmarks. Across all the benchmarks in this comparison, we can state that the performance in the image classification, object detection, speech to text and language processing workloads packaged with NVIDIA L4 GPUs for both servers provide exceptional performance.
Figure 7: Dell PowerEdge XR4520c and PowerEdge XR7620 servers across the ResNet50, RetinaNet, RNNT, and BERT 99 Single and Multi-stream benchmarks
Figure 8: Dell PowerEdge XR4520c and PowerEdge XR7620 servers across the ResNet50, RetinaNet, RNNT, and BERT 99 Offline benchmarks
Like ResNet50 and RetinaNet, the 3D-Unet benchmark falls under the vision area but focuses on the medical image segmentation task. The following figures show identical performance of the two servers in both the default and high accuracy modes in the Single-stream and Offline scenarios.
Figure 9: Dell PowerEdge XR4520c and PowerEdge XR7620 servers across 3D-Unet Single-stream
Figure 10: Dell PowerEdge XR4520c and PowerEdge XR7620 server across 3D-Unet Offline
Dell PowerEdge XR5610 power submission
In the MLPerf Inference v3.0 round of submissions, Dell Technologies made a power submission under the preview category for the Dell PowerEdge XR5610 server with the NVIDIA L4 GPU. For the v3.1 round of submissions, Dell Technologies made another power submission for the same server in the closed edge category. As shown in the following table, the detailed configurations of both the systems across the rounds of submissions show that the hardware remained consistent, but that the software stack was updated. In terms of system performance per watt, the PowerEdge XR 5610 server claims the top spot in image classification, object detection, speech-to-text, language processing, and medical image segmentation workloads.
Table 2: MLPerf Inference v3.0 and v3.1 system configuration of the Dell PowerEdge XR5610 server
Platform | Dell PowerEdge XR5610 (1x L4, MaxQ, TensorRT) v3.0 | Dell PowerEdge XR5610 (1x L4, MaxQ, TensorRT) v3.1 |
MLPerf system ID | XR5610_L4x1_TRT_MaxQ | XR5610_L4x1_TRT_MaxQ |
Operating system | CentOS 8.2 | |
CPU | Intel(R) Xeon(R) Gold 5423N CPU @ 2.10 GHz | |
Memory | 256 GB | |
GPU | NVIDIA L4 | |
GPU count | 1 | |
Software stack | TensorRT 8.6.0 CUDA 12.0 cuDNN 8.8.0 Driver 515.65.01 DALI 1.17.0 | TensorRT 9.0.0 CUDA 12.2 cuDNN 8.9.2 Driver 525.105.17 DALI 1.28.0
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The power submission includes extra power results in each submission. For each submitted benchmark, there is a power metric that is paired with it. The metric for the Single-stream and Multi-stream performance results is Latency in milliseconds and the corresponding power consumption is noted in millijoules (mj). The Offline performance numbers are recorded in samples per second(samples/s), and the corresponding power readings are delivered in watts. The following table shows a breakdown for the calculations for queries per millijoules and samples/s per watt have been calculated.
Table 3: Breakdown of reading a power submission
Scenario | Performance metric | Power metric | Performance per unit of energy |
Single Stream | Latency (ms) | Millijoules (mj) | 1 query/mj -> queries/mj |
Multi Stream | Latency (ms) | Millijoules (mj) | 8 queries/mj -> queries/mj |
Offline | Samples/s | Watts | Samples/s / Watts -> performance per Watt |
The following figure shows the improvements in the performance per energy used on the Dell PowerEdge XR5610 server across the v3.1 and v3.0 rounds of submission. Across all the benchmarks, the server extracted double the performance per energy. For the RNNT Single-stream benchmark, the servers showed a brilliant performance jump of close to five times greater. The performance improvements came from hardware and software optimizations. Also, BIOS firmware upgrades also contributed significantly.
Figure 11: Dell PowerEdge XR5610 with NVIDIA L4 GPU power submission for v3.1 compared to v3.0
The following figure shows the Single-stream and Multi-stream latency results from the Dell PowerEdge XR5610 server:
Figure 12: Dell PowerEdge XR5610 NVIDIA L4 GPU L4 v3.1 server
Conclusion
Both the Dell PowerEdge XR4520c and Dell PowerEdge XR7620 servers continue to showcase excellent performance in the edge suite for MLPerf Inference. The Dell PowerEdge XR5610 server showed a consistent doubling in performance per energy across all benchmarks confirming itself as a power efficient server option. Built for the edge, the Dell PowerEdge XR portfolio proves to be an outstanding option with consistent performance in the MLPerf Inference v3.1 submission. As the need for edge computing continues to grow, the MLPerf Inference edge suite shows that Dell PowerEdge servers continue to be an excellent option for any Artificial Intelligence workload.
MLCommons results
https://mlcommons.org/en/inference-edge-31/
MLPerf Inference v3.1 system IDs:
- 3.1-0072 - Dell PowerEdge XR4520c (1x L4, TensorRT)
- 3.1-0073 - Dell PowerEdge XR5610 (1x L4, MaxQ, TensorRT)
- 3.1-0074 - Dell PowerEdge XR7620 (1x L4, TensorRT)
The MLPerf™ name and logo are trademarks of MLCommons Association in the United States and other countries. All rights reserved. Unauthorized use strictly prohibited. See www.mlcommons.org for more information.
Related Blog Posts
Promising MLPerf™ Inference 3.1 Performance of Dell PowerEdge XE8640 and XE9640 Servers with NVIDIA H100 GPUs
Wed, 04 Oct 2023 20:54:55 -0000
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Abstract
The recent release of MLPerf Inference v3.1 showcased the latest performance results from Dell's new PowerEdge XE8640 and PowerEdge XE9640 servers, and another submission from the PowerEdge R760xa server. The data underscores the outstanding performance of PowerEdge servers. These benchmarks illustrate the surging demand for compute power, with PowerEdge servers consistently emerging on top across various models, claiming numerous top titles. This blog examines the expected performance for image classification, object detection, question answering, speech recognition, medical image segmentation and summarization, focusing specifically on the capabilities of the PCIe and SXM form factor NVIDIA H100 Tensor Core GPUs in the new generation PowerEdge systems.
Overview of top title results
The PowerEdge XE8640 and XE9640 servers won several #1 titles.
For instance, the PowerEdge XE8640 server emerged as a winner in all benchmarks in the data center suite such as image classification, object detection, question answering, speech recognition, medical image segmentation, and summarization relative to other systems having four NVIDIA H100 SXM GPUs. The PowerEdge XE9640 server received #1 titles for all benchmarks previously mentioned relative to other liquid-cooled systems having four NVIDIA H100 SXM GPUs.
Comparison from the previous rounds of submission
The following figure shows the improvement customers can derive by using the new generation PowerEdge XE8640 and XE9640 servers from our previous generation PowerEdge XE8545 server.
Figure 1. Relative performance of PowerEdge XE8640 and PowerEdge XE9640 servers using the PowerEdge XE8545 server as a baseline reference (for the Y axis, the higher the better)
The graph shows that the relative performance improvement from the PowerEdge XE8545 server with four NVIDIA A100 SXM Tensor Core GPUs as a baseline (from MLPerf Inference v3.0) and the new generation severs such as the PowerEdge XE8640 and PowerEdge XE9640 servers using NVIDIA H100 Tensor Core GPUs. The improvement in performance is substantial, as evident from the graph. End users can derive a two- to four-times improvement in performance for different tasks in MLPerf Inference benchmarks. We see relatively higher performance with BERT benchmarks because of the NVIDIA H100 GPU’s FP8 support.
Comparing air-cooled and liquid-cooled servers
The following figure shows the raw performance of PowerEdge XE8640 and XE9640 servers; this graph and the following graph provide relative scores. The graph includes all the benchmarks in the Inference closed data center suite that we submitted. Note that different benchmarks have different scales. All the benchmarks are presented in one graph, therefore, the y-axis is expressed logarithmically.
Figure 2. Performance of PowerEdge XE8640 and PowerEdge XE9640 servers
PowerEdge XE8640 and XE9640 servers are both great choices for inference workloads with four NVIDIA H100 SXM Tensor Core GPUs. The PowerEdge XE9640 server is a liquid-cooled server and the PowerEdge XE8640 server is an air-cooled server. The following figure shows the difference in performance between these systems; they both performed optimally. Both systems have similar effective throughput and render excellent performance as the CPU and GPU configurations are the same.
Figure 3. Performance difference between PowerEdge XE9640 and XE8640 servers using the PowerEdge XE9640 server as a baseline
Impact of SXM over PCIe form factors
The following figure shows the performance of the PowerEdge R760xa server with NVIDIA H100 PCIe GPUs as the baseline and shows the performance improvement of PowerEdge XE9640 and PowerEdge XE8640 servers with NVIDIA H100 Tensor Core SXM GPUs. The graph demonstrates that the PowerEdge XE8640 server with NVIDIA H100 SXM GPUs performs approximately 1.25 to 1.7 times better than the PowerEdge R760xa server with NVIDIA H100 PCIe GPUs.
Figure 4. Performance difference between PowerEdge XE9640 and XE8640 servers with 4x H100 SXM and PowerEdge R760xa server with 4x H100 PCIe as a baseline
Because the NVIDIA H100 SXM GPUs have higher Thermal Design Power (TDP), if high performance is imperative, then using NVIDIA SXM GPUs is a great choice.
Comparing efficiency of new and previous generation servers
The following figure shows the performance of the previous generation PowerEdge XE8545 server with NVIDIA A100 SXM GPUs compared to the new generation servers such as the PowerEdge R760xa server with the NVIDIA H100 PCIE form factor and the PowerEdge XE8640 and XE9640 servers with the NVIDIA H100 SXM form factor. We see that all the new generation servers rendered higher performance. Furthermore, our new generation PowerEdge R760xa server with four NVIDIA H100 PCIe GPUs is more power efficient than our previous generation PowerEdge XE8545 server with four NVIDIA A100 SXM GPUs. This result is because NVIDIA A100 SXM GPUs have higher TDP relative to the NVIDIA H100 PCIe GPU.
Figure 5. Relative performance of PowerEdge R760xa, PowerEdge XE9640, and PowerEdge XE8640 servers using the PowerEdge XE8545 server as a baseline
Hardware overview
The following sections describe the system components. The appendix lists the system configurations in the benchmark.
Dell PowerEdge XE8640 server
The PowerEdge XE8640 server is an air-cooled 4U server that accelerates traditional AI training and inferencing, modeling, simulation, and other high-performance computing (HPC) applications with optimized compute, turning data and automating insights into outcomes with a four-way GPU platform. Its powerful architecture and the power of two 4th Generation Intel Xeon processors with a high core count of up to 56 cores and the latest on-chip innovations to boost AI and machine learning operations.
The following figure shows the PowerEdge XE8640 server:
Figure 6. Dell PowerEdge XE8640 server
Dell PowerEdge XE9640 server
The PowerEdge XE9640 server is a purpose-built direct liquid-cooled (DLC) 2U server for AI and HPC workloads. NVIDIA NVLink and Intel Xelink technologies in the PowerEdge XE9640 server allow seamless communication between the GPUs, pooling their memory and cores to tackle memory-coherent workloads such as large language models (LLM) efficiently.
The following figure shows the PowerEdge XE9640 server:
Figure 7. Dell PowerEdge XE8640
NVIDIA H100 Tensor core GPU
The NVIDIA H100 GPU is an integral part of the NVIDIA data center platform. Built for AI, HPC, and data analytics, the platform accelerates over 3,000 applications, and is available everywhere from the data center to the edge, delivering both dramatic performance gains and cost-saving opportunities. The NVIDIA H100 Tensor Core GPU delivers unprecedented performance, scalability,
and security for every workload. With NVIDIA® NVLink® Switch System, up to 256
NVIDIA H100 GPUs can be connected to accelerate exascale workloads, while the dedicated
Transformer Engine supports trillion-parameter language models. The NVIDIA H100 GPU uses
breakthrough innovations in the NVIDIA Hopper™ architecture to deliver industry-leading conversational AI, speeding up large language models by 30 times over the previous generation.
The following figure shows the NVIDIA H100 PCIe accelerator:
Figure 8. NVIDIA H100 PCIe accelerator
The following figure shows the NVIDIA H100 SXM accelerator:
Figure 9. NVIDIA H100 SXM accelerator
Conclusion
The key takeaways include:
- Both the Dell PowerEdge XE8640 and Dell PowerEdge XE9640 servers are an excellent choice for inference. The performance of the air-cooled PowerEdge XE8640 server is almost identical to the liquid-cooled PowerEdge XE9640 server. While the PowerEdge XE9640 server is a 2U server, it requires additional cooling unit attachments. It is a good choice if there are space and temperature constraints, otherwise the PowerEdge XE8640 server is a great choice.
- PowerEdge XE8640 and PowerEdge 9640 servers have received several top titles. They are clear leaders in inference compute.
- New generation PowerEdge XE8640 and PowerEdge XE9640 servers with NVIDIA H100 GPUs have delivered 2- to 4-times improvement relative to the previous generation PowerEdge XE8545 server with NVIDIA A100 GPUs. Upgrading from the PowerEdge XE8545 sever would render higher performance.
- The PowerEdge XE9640 and PowerEdge XE8640 servers with four NVIDIA H100 SXM form-factor GPUs are significantly more effective than the PowerEdge R760xa server with four NVIDIA H100 PCIe GPUs by a factor of 1.25 to 1.7 times.
Our submission results to MLPerf Inference since its inception have continuously demonstrated significant performance improvements. We have submitted to different tasks to provide customers with a wide spectrum of possible results to review. This round marked a new and the first submission to MLPerf with PowerEdge XE8640 and XE9640 servers. Customers can rely on these high compute machines for their fast/low latency inference needs. If constrained by TDP or other factors, the PowerEdge R760xa server with the PCIe form factor is an excellent choice on which to run inference workloads.
Appendix
The following table lists the system configuration details for the servers described in this blog:
Table 1. System configurations
| Dell PowerEdge XE 8640 (4x NVIDIA H100-SXM-80GB, TensorRT) | Dell PowerEdge XE 9640 (4x H100-SXM-80GB, TensorRT) | Dell PowerEdge R760xa (4x H100-PCIe-80GB, TensorRT) | Dell PowerEdge XE 8545 (4x A100-SXM-80GB, TensorRT) |
MLPerf submission ID | 3.1-0066 | 3.1-0067 | 3.1-0064 | 3.0-0011 |
MLPerf system ID | XE8640_H100_SXM_80GBx4_TRT | XE9640_H100_SXM_80GBx4_TRT | R760xa_H100_PCIe_80GBx4_TRT | XE8545_A100_SXM4_80GBx4_TRT |
Operating system | Rocky Linux 9.1 | Ubuntu 22.04 | Ubuntu 20.04.4 | Ubuntu 22.04 |
CPU | Intel Xeon Platinum 8480 | Intel Xeon Platinum 8480+ | Intel Xeon Platinum 8480+ | AMD EPYC 7763 |
Memory | 1 TB | 1 TB | 2 TB | 2 TB |
GPU | NVIDIA H100 SXM 80 GB | NVIDIA H100 PCIE 80 GB | NVIDIA A100 SXM 80 GB CTS | |
GPU count | 4 | |||
Software stack | TensorRT 9.0.0 CUDA 12.2 | TensorRT 8.6.0 CUDA 12.2 | ||
MLCommons results
MLPerf system IDs:
- ID 3.0-0011
- ID 3.1-0064
- ID 3.1-0066
- ID 3.1-0067
Note: We reran the RetinaNet Offline benchmark for the PowerEdge R760xa server and the DLRMv2 benchmark for the PowerEdge XE8640 server to reflect the correct performance that the servers can render. Only these two results are not official due to MLCommons rules.
The MLPerf™ name and logo are trademarks of MLCommons Association in the United States and other countries. All rights reserved. Unauthorized use strictly prohibited. See www.mlcommons.org for more information.
Comparing the NVIDIA H100 and A100 GPUs in Dell PowerEdge R760xa and R750xa Servers
Wed, 04 Oct 2023 16:47:00 -0000
|Read Time: 0 minutes
Abstract
Dell Technologies recently submitted results to the MLPerf™ Inference v3.1 benchmark suite. This blog highlights Dell Technologies’ closed division submission made for the Dell PowerEdge R760xa and Dell PowerEdge R750xa servers with NVIDIA H100 and NVIDIA A100 GPUs.
Introduction
This blog provides comparisons that draw relevant conclusions about the performance improvements that are achieved on the Dell PowerEdge R760xa server with the NVIDIA H100 GPU compared to their respective predecessors, the Dell PowerEdge R750xa server with the NVIDIA A100 GPU. In the Dell PowerEdge R760xa server section of this blog, we compare the performance of the PowerEdge R760xa server to the PowerEdge R750xa server while keeping the NVIDIA H100 GPU constant to demonstrate the improvement of the new generation of PowerEdge servers. Also, we compared the performance of the PowerEdge R760xa server with the NVIDIA H100 GPU to the PowerEdge R750xa server with the NVIDIA A100 GPU to showcase the server plus the GPU generation-to-generation improvements. In the Dell PowerEdge R750xa server section of this blog, we kept the server constant and compared the performance of the NVIDIA H100 GPU to the NVIDIA A100 GPU. For an additional angle, we held the PowerEdge R750xa server and the NVIDIA A100 GPU constant to showcase the performance improvements delivered by software stack updates.
System Under Test (SUT) configuration
Table 1: SUT configuration of the Dell PowerEdge R760xa and Dell PowerEdge R750xa servers for MLPerf Inference v3.1 and v3.0
| Platform | R750xa | R750xa | R760xa |
| MLPerf Version | V3.0 | V3.1 | V3.1 |
| GPU | NVIDIA A100 PCIe 80 GB | NVIDIA A100 PCIe 80 GB NVIDIA H100 PCIe 80 GB | NVIDIA H100 PCIe 80 GB |
| GPU Count | 4 | ||
| MLPerf System ID | R750xa_A100_PCIE_80GBx4_TRT | R750xa_A100_PCIe_80GBx4_TRT R750xa_H100_PCIe_80GBx4_TRT | R760xa_H100_PCIe_80GBx4_TRT |
| CPU | Intel Xeon Gold 6338 CPU @ 2.00 GHz | Intel Xeon Platinum 8480+ | |
| Memory | 512 GB | 512 GB 1 TB | 2 TB |
| Software Stack | TensorRT 8.6 CUDA 12.0 cuDNN 8.8.0 Driver 525.85.12 DALI 1.17.0 | TensorRT 9.0.0 CUDA 12.2 cuDNN 8.9.2 Driver 535.86.10 DALI 1.28.0 | |
The following table shows the technical specifications of the NVIDIA H100 and NVIDIA A100 GPUs:
Table 2: Technical specification comparison of the NVIDIA H100 and NVIDIA A100 GPUs
GPU | NVIDIA A100 | NVIDIA H100 | ||||||
Form factor | SXM4 | PCIe Gen4 | SXM4 | PCIe Gen4 | PCIe Gen5 | NVL PCIe Gen5 | SXM5 | |
GPU architecture | Ampere | Hopper | ||||||
CUDA cores | 6912 | 14592 | 2x 16895 | 16895 | ||||
Memory size | 40 GB | 80 GB | 80 GB | 2x 94 GB (188 GB) | 80 GB | 94 GB | ||
Memory type | HBM2e | HBM2 | HBM2e | HBM2e | HBM3 | HBM2e | ||
Base clock | 1095 MHz | 765 MHz | 1275 MHz | 1065 MHz | 1095 MHz | 1080 MHz | 1590 MHz | 1605 MHz |
Boost clock | 1410 MHz | 1755 MHz | 1785 MHz | 1980 MHz | ||||
Memory clock | 1215 MHz | 1593 MHz | 1512 MHz | 1593 MHz | 2619 MHz | 1593 MHz | ||
MIG support | Yes | Yes/2nd Gen | ||||||
Peak memory bandwidth | 1555 GB/s | 2039 GB/s
| 1935 GB/s | 2039 GB/s | 3938 GB/s | 3352 GB/s | 2359 GB/s | |
Total board power | 400 W | 250 W | 400 W | 300 W | 310/350 W | 400 W | 700 W | |
Dell PowerEdge R760xa server
The PowerEdge R760xa server shines as an Artificial Intelligence (AI) workload server with its cutting-edge inferencing capabilities. This server represents the pinnacle of performance in the AI inferencing space with its processing prowess enabled by Intel Xeon Platinum processors and NVIDIA H100 PCIe 80 GB GPUs. Coupled with NVIDIA TensorRT and CUDA 12.2, the PowerEdge R760xa server is positioned perfectly for any AI workload including but not limited to Large Language Models, computer vision, Natural Language Processing, robotics, and edge computing. Whether you are processing image recognition tasks, natural language understanding, or deep learning models, the PowerEdge R760xa server provides the computational muscle for reliable, precise, and fast results.
Figure 1: Front view of the Dell PowerEdge R760xa server
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Figure 2: Top view of the Dell PowerEdge R760xa server
The results in the following figures are represented as percentage differences while maintaining a single SUT as the baseline. To determine the percentage difference between the two results, we subtracted the performance value achieved on the first server from the performance value achieved on the second server. We divided the difference by the performance achieved on the second server and multiplied it by 100 to get a percentage. By applying this formula, we obtain the performance delta between the second and first server. This result provides an easy-to-read comparison across two systems and several benchmarks.
The following figure shows the percent difference between the PowerEdge R760xa and PowerEdge R750xa servers while maintaining the NVIDIA H100 GPU constant. Both results were collected from the latest official MLPerf Inference v.3.1 submission with the identical software stack. Across all the benchmarks, the PowerEdge R760xa server comprehensively outperformed its predecessor. The PowerEdge R760xa server shined in the Natural Language Processing task with a noticeable 15 percent improvement. On average, it performed approximately 6 percent better for all workloads.
Figure 3: Percentage difference between the Dell PowerEdge R760xa server with the NVIDIA H100 GPU and the Dell PowerEdge R750xa server with the NVIDIA H100 GPU for the v3.1 submission
The following figure shows a comparison of the PowerEdge R760xa server with the NVIDIA H100 GPU to the PowerEdge R750xa server with the NVIDIA A100 GPU. This comparison is expected to yield the highest delta in performance due to the hardware upgrades of both the server and GPU. Both submissions were made to the MLPerf Inference v3.1 round in which the software stack was kept the same. The PowerEdge R760xa server paired with the NVIDIA H100 GPU thoroughly outperformed its predecessor in all workloads. In the high accuracy category of the Natural Language Processing workload, the PowerEdge R760xa server boasts an impressive 178 percent and 197 percent performance improvement in the Server and Offline modes respectively. On average, the newer configuration showcased a noteworthy 71 percent improvement across all the benchmarks.
Figure 4: Percentage difference between the Dell PowerEdge R760xa server with the NVIDIA H100 GPU and the Dell PowerEdge R750xa server with the NVIDIA A100 GPU for v3.1
Dell PowerEdge R750xa server
The PowerEdge R750xa server is a perfect blend of technological prowess and innovation. This server is equipped with Intel Xeon Gold processors as well as with the latest NVIDIA GPUs. The PowerEdge R760xa server has been designed for the most demanding AI/ML/DL workloads as it is compatible with the latest NVIDIA TensorRT engine and CUDA version. With up to nine PCIe Gen4 slots and availability in a 1U or 2U configuration, the PowerEdge R750xa server is an excellent option for any demanding workload.
Figure 5: Front view of the Dell PowerEdge R750xa server
Figure 6: Rear view of the Dell PowerEdge R750xa server
For the following comparison, the Dell PowerEdge R750xa server is held constant but the GPU is updated from the NVIDIA A100 GPU to the NVIDIA H100 GPU. This comparison is useful if you are interested in keeping the server that you already have but are upgrading the GPU. As expected, the server with the NVIDIA H100 GPU shows significant performance improvements across all the workloads. Similar to the previous comparison, the high accuracy Natural Language Processing task on the NVIDIA H100 GPU shows promising performance improvements. In the high accuracy Server scenario for BERT, the NVIDIA H100 GPU showed a 156 percent improvement and in the Offline scenario a 174 percent improvement. On average, the PowerEdge R750xa server paired with the NVIDIA H100 GPU performed approximately 60 percent better than its GPU predecessor.
Figure 7: Percentage difference between the Dell PowerEdge R750xa H100 and Dell PowerEdge R750xa A100 for MLPerf Inference v3.1
The comparison shown in the following figure is an interesting comparison across two rounds of submissions. The hardware for the SUT is identical with the Dell PowerEdge R750xa server paired with the NVIDIA A100 GPU. The performance delta from this graph can be attributed to the changes in the software stack. For the vision tasks, RetinaNet and 3D-UNet, the NVIDIA H100 GPU showed a considerable improvement in performance. For the BERT Server scenario, the performance is approximately the same. However, for the BERT Offline scenario in both the default and high accuracy modes, there was a slight regression in performance. This result can be attributed to regressions in the BERT model.
Figure 8: Percentage difference between the Dell PowerEdge R750xa server with the NVIDIA A100 GPU v3.1 submission and the Dell PowerEdge R750xa server with the NVIDIA A100 GPU v3.0 submission
Conclusion
The MLPerf Inference submissions always elicit insightful comparisons. This blog highlighted these comparisons between the MLPerf Inference v3.1 and v3.0 rounds of submission:
- A generation-to-generation comparison of the Dell PowerEdge R760xa server and the Dell PowerEdge R750xa server while keeping the GPU constant on average boasts an impressive 6.22 percent performance improvement.
- An upgrade of the server as well as the GPU from the Dell PowerEdge R750xa server paired with the NVIDIA A100 GPU to the Dell PowerEdge R760xa server paired with the NVIDIA H100 GPU shows a noteworthy boost in performance. You can expect about an average of 71 percent increase in performance across benchmarks by upgrading both the server and the GPU.
- While maintaining the Dell PowerEdge R750xa server and upgrading the GPU from the NVIDIA A100 GPU to the NVIDIA H100 GPU, you can expect an approximate 60 percent increase in performance across benchmarks.
- While maintaining the same SUT across rounds with the Dell PowerEdge R750xa server and the NVIDIA A100 GPU, you can expect on average an 11.36 percent increase in improvement for RetinaNet, 3D-UNet, and RNNT tasks, thanks to software improvements. However, there are minor regressions in performance in the BERT benchmark.
Across the first three comparisons, a pattern of improvement in the Natural Language Processing task was noticeable. With the advent of new Large Language Models, the Dell PowerEdge server is positioned well to handle Generative AI workloads. For the last comparison, we kept the Dell PowerEdge R750xa server and NVIDIA A100 GPU consistent but looked at the performance across different rounds of submission.
MLCommons™ results
Note: We ran the RetinaNet Offline results for the Dell PowerEdge R760xa and Dell PowerEdge R750xa servers with the NVIDIA H100 GPU again after the submission with a larger GPU batch size. These results significantly improved the performance and are a true representation of Dell servers as we saw a 78 percent and 114 percent increase in performance on the PowerEdge R760xa server and PowerEdge R750xa servers respectively. For the Dell PowerEdge R760xa server with four NVIDIA H100 GPUs, the RetinaNet Offline results improved from 2069.79 to 4550.67. The RetinaNet Offline results for the system ID 3.1-0063 and 3.1-0065 submissions are not official due to MLCommons rules because they were rerun after the submission and not officially submitted before the deadline.
MLPerf Inference v3.1 and v3.0 system IDs:
- 3.1-0058, 3.1-0061 Dell PowerEdge R750xa (4x A100-PCIe-80GB, TensorRT)
- 3.1-0062 Dell PowerEdge R750xa (4x H100-PCIe-80GB, TensorRT)
- 3.1-0064 Dell PowerEdge R760xa (4x H100-PCIe-80GB, TensorRT)
- 3.0-0008 Dell PowerEdge R750xa (4x A100-PCIe-80GB, TensorRT)
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