
New Frontiers—Dell EMC PowerEdge R750xa Server with NVIDIA A100 GPUs
Tue, 01 Jun 2021 20:18:04 -0000
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Dell Technologies has released the new PowerEdge R750xa server, a GPU workload-based platform that is designed to support artificial intelligence, machine learning, and high-performance computing solutions. The dual socket/2U platform supports 3rd Gen Intel Xeon processors (code named Ice Lake). It supports up to 40 cores per processor, has eight memory channels per CPU, and up to 32 DDR4 DIMMs at 3200 MT/s DIMM speed. This server can accommodate up to four double-width PCIe GPUs that are located in the front left and the front right of the server.
Compared with the previous generation PowerEdge C4140 and PowerEdge R740 GPU platform options, the new PowerEdge R750xa server supports larger storage capacity, provides more flexible GPU offerings, and improves the thermal requirement
Figure 1 PowerEdge R750xa server
The NVIDIA A100 GPUs are built on the NVIDIA Ampere architecture to enable double precision workloads. This blog evaluates the new PowerEdge R750xa server and compares its performance with the previous generation PowerEdge C4140 server.
The following table shows the specifications for the NVIDIA GPU that is discussed in this blog and compares the performance improvement from the previous generation.
Table 1 NVIDIA GPU specifications
PCIe | Improvement | ||
GPU name | A100 | V100 |
|
GPU architecture | Ampere | Volta | - |
GPU memory | 40 GB | 32 GB | 60% |
GPU memory bandwidth | 1555 GB/s | 900 GB/s | 73% |
Peak FP64 | 9.7 TFLOPS | 7 TFLOPS | 39% |
Peak FP64 Tensor Core | 19.5 TFLOPS | N/A | - |
Peak FP32 | 19.5 TFLOPS | 14 TFLOPS | 39% |
Peak FP32 Tensor Core | 156 TFLOPS 312 TFLOPS* | N/A | - |
Peak Mixed Precision FP16 ops/ FP32 Accumulate | 312 TFLOPS 624 TFLOPS* | 125 TFLOPS | 5x |
GPU base clock | 765 MHz | 1230 MHz | - |
Peak INT8 | 624 TOPS 1,248 TOPS* | N/A | - |
GPU Boost clock | 1410 MHz | 1380 MHz | 2.1% |
NVLink speed | 600 GB/s | N/A | - |
Maximum power consumption | 250 W | 250 W | No change |
Test bed and applications
This blog quantifies the performance improvement of the GPUs with the new PowerEdge GPU platform.
Using a single node PowerEdge R750xa server in the Dell HPC & AI Innovation Lab, we derived all results presented in this blog from this test bed. This section describes the test bed and the applications that were evaluated as part of the study. The following table provides test environment details:
Table 2 Server configuration
Component | Test Bed 1 | Test Bed 2 |
Server | Dell PowerEdge R750xa
| Dell PowerEdge C4140 configuration M |
Processor | Intel Xeon 8380 | Intel Xeon 6248 |
Memory | 32 x 16 GB @ 3200MT/s | 16 x 16 GB @ 2933MT/s |
Operating system | Red Hat Enterprise Linux 8.3 | Red Hat Enterprise Linux 8.3 |
GPU | 4 x NVIDIA A100-PCIe-40 GB GPU | 4 x NVIDIA V100-PCIe-32 GB GPU |
The following table provides information about the applications and benchmarks used:
Table 3 Benchmark and application details
Application | Domain | Version | Benchmark dataset |
High-Performance Linpack | Floating point compute-intensive system benchmark | xhpl_cuda-11.0-dyn_mkl-static_ompi-4.0.4_gcc4.8.5_7-23-20 | Problem size is more than 95% of GPU memory |
HPCG | Sparse matrix calculations | xhpcg-3.1_cuda_11_ompi-3.1 | 512 * 512 * 288
|
GROMACS | Molecular dynamics application | 2020 | Ligno Cellulose Water 1536 Water 3072 |
LAMMPS | Molecular dynamics application | 29 October 2020 release | Lennard Jones |
LAMMPS
Large-Scale Atomic/Molecular Massively Parallel simulator (LAMMPS) is distributed by Sandia National Labs and the US Department of Energy. LAMMPS is open-source code that has different accelerated models for performance on CPUs and GPUs. For our test, we compiled the binary using the KOKKOS package, which runs efficiently on GPUs.
Figure 2 LAMMPS Performance on PowerEdge R750xa and PowerEdge C4140 servers
With the newer generation GPUs, this application improves 2.4 times compared to single GPU performance. The overall performance from a single server improved twice with the PowerEdge R750xa server and NVIDIA A100 GPUs.
GROMACS
GROMACS is a free and open-source parallel molecular dynamics package designed for simulations of biochemical molecules such as proteins, lipids, and nucleic acids. It is used by a wide variety of researchers, particularly for biomolecular and chemistry simulations. GROMACS supports all the usual algorithms expected from modern molecular dynamics implementation. It is open-source software with the latest versions available under the GNU Lesser General Public License (LGPL).
Figure 3 GROMACS performance on PowerEdge C4140 and r750xa servers
With the newer generation GPUs, this application improved approximately 1.5 times across the dataset compared to single GPU performance. The overall performance from a single server improved 1.5 times with a PowerEdge R750xa server and NVIDIA A100 GPUs.
High-Performance Linpack
High-Performance Linpack (HPL) needs no introduction in the HPC arena. It is a widely used standard benchmark tests in the industry.
Figure 4 HPL Performance on the PowerEdge R750xa server with A100 GPU and PowerEdge C4140 server with V100 GPU
Figure 5 Power use of the HPL running on NVIDIA GPUs
From Figure 4 and Figure 5, the following results were observed:
- Performance—For GPU count, the NVIDIA A100 GPU demonstrates twice the performance of the NVIDIA V100 GPU. Higher memory size, double precision FLOPS, and a newer architecture contribute to the improvement for the NVIDIA A100 GPU.
- Scalability—The PowerEdge R750xa server with four NVIDIA A100-PCIe-40 GB GPUs delivers 3.6 times higher HPL performance compared to one NVIDIA A100-PCIE-40 GB GPU. The NVIDIA A100 GPUs scale well inside the PowerEdge R750xa server for the HPL benchmark.
- Higher Rpeak—The HPL code on NVIDIA A100 GPUs uses the new double-precision Tensor cores. The theoretical peak for each GPU is 19.5 TFlops, as opposed to 9.7 TFlops.
- Power—Figure 5 shows power consumption of a complete HPL run with the PowerEdge R750xa server using four A100-PCIe GPUs. This result was measured with iDRAC commands, and the peak power consumption was observed as 2022 Watts. Based on our previous observations, we know that the PowerEdge C4140 server consumes approximately 1800 W of power.
HPCG
Figure 6 Scaling GPU performance data for HPCG Benchmark
As discussed in other blogs, high performance conjugate gradient (HPCG) is another standard benchmark to test data access patterns of sparse matrix calculations. From the graph, we see that the HPCG benchmark scales well with this benchmark resulting in 1.6 times performance improvement over the previous generation PowerEdge C4140 server with an NVIDIA V100 GPU.
The 72 percent improvement in memory bandwidth of the NVIDIA A100 GPU over the NVIDIA V100 GPU contributes to the performance improvement.
Conclusion
In this blog, we introduced the latest generation PowerEdge R750xa platform and discussed the performance improvement over the previous generation PowerEdge C4140 server. The PowerEdge R750xa server is a good option for customers looking for an Intel Xeon scalable CPU-based platform powered with NVIDIA GPUs.
With the newer generation PowerEdge R750xa server and NVIDIA A100 GPUs, the applications discussed in this blog show significant performance improvement.
Next steps
In future blogs, we plan to evaluate NVLINK bridge support, which is another important feature of the PowerEdge R750xa server and NVIDIA A100 GPUs.
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Overview of MLPerf™ Inference v2.0 Results on Dell Servers
Fri, 09 Sep 2022 15:15:36 -0000
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Dell Technologies has been an active participant in the MLCommons™ Inference benchmark submission since day one. We have completed five rounds of inference submission.
This blog provides an overview of the latest results of MLPerf Inference v2.0 closed data center, closed data center power, closed edge, and closed edge power categories on Dell servers from our HPC & AI Innovation Lab. It shows optimal inference and power (performance per watt) performance for Dell GPU-based servers (DSS 8440, PowerEdge R750xa, PowerEdge XE2420, PowerEdge XE8545, and PowerEdge XR12). The previous blog about MLPerf Inference v1.1 performance results can be found here.
What is new?
- There were 3,800 performance results for this round compared to 1,800 performance results for v1.1. Additionally, 885 systems in v2.0 compared to 424 systems in v1.1 shows that there were more than twice the systems submitted for this round.
- For the 3D U-Net benchmark, the dataset now used is the KiTs 2019 Kidney Tumor Segmentation set.
- Early stopping was introduced in this round to replace a deterministic minimum query count with a function that dynamically determines when further runs are not required to identify additional performance gain.
Results at a glance
Dell Technologies submitted 167 results to the various categories. The Dell team made 86 submissions to the closed data center category, 28 submissions to the closed data center power category, and 53 submissions to the closed edge category. For the closed data center category, the Dell team submitted the second most results. In fact, Dell Technologies submitted results from 17 different system configurations with the NVIDIA TensorRT and NVIDIA Triton inference engines. Among these 17 configurations, the PowerEdge XE2420 server with T4 and A30 GPUs and the PowerEdge XR12 server with the A2 GPU were two new systems that have not been submitted before. Additionally, Dell Technologies submitted to the reintroduced Multiterm scenario. Only Dell Technologies submitted results for different host operating systems.
Noteworthy results
Noteworthy results include:
- The PowerEdge XE8545 and R750xa servers yield Number One results for performance per accelerator with NVIDIA A100 GPUs. The use cases for this top classification include Image Classification, Object Detection, Speech-to-text, Medical Imaging, Natural Language Processing, and Recommendation.
- The DSS 8440 server yields Number Two results for system performance for multiple benchmarks including Speech-to-text, Object Detection, Natural Language Processing, and Medical Image Segmentati on uses cases among all submissions.
- The PowerEdge R750xa server yields Number One results for the highest system performance for multiple benchmarks including Image Classification, Object Detection, Speech-to-text, Natural Language Processing, and Recommendation use cases among all the PCIe-based GPU servers.
- The PowerEdge XE8545 server yields Number One results for the lowest multistrand latency with NVIDIA Multi-Instance GPU (MIG) in the edge category for the Image Classification and Object Detection use cases.
- The PowerEdge XE2420 server yields Number One results for the highest T4 GPU inference results for the Image Classification, Speech-to-text, and Recommendation use cases.
- The PowerEdge XR12 server yields Number One results for the highest performance per watt with NVIDIA A2 GPU results in power for the Image Classification, Object Detection, Speech-to-text, Natural Language Processing, and Recommendation use cases.
MLPerf Inference v2.0 benchmark results
The following graphs highlight the performance metrics for the Server and Offline scenarios across the various benchmarks from MLCommons. Dell Technologies presents results as an method for our customers to identify options to suit their deep learning application demands. Additionally, this performance data serves as a reference point to enable sizing of deep learning clusters. Dell Technologies strives to submit as many results as possible to offer answers to ensure that customer questions are resolved.
For the Server scenario, the performance metric is queries per second (QPS). For the Offline scenario, the performance metric is Offline samples per second. In general, the metrics represent throughput, and a higher throughput indicates a better result. In the following graphs, the Y axis is an exponentially scaled axis representing throughput and the X axis represents the systems under test (SUTs) and their corresponding models. The SUTs are described in the appendix.
Figure 1 through Figure 6 show the per card performance of the various SUTs on the ResNet 50, BERT, SSD, 3dUnet, RNNT, and DLRM modes respectively in the Server and Offline scenarios:
Figure 1: MLPerf Inference v2.0 ResNet 50 per card results
Figure 2: MLPerf Inference v2.0 BERT default and high accuracy per card results
Figure 3: MLPerf Inference v2.0 SSD-ResNet 34 per card results
Figure 4: MLPerf Inference v2.0 3D U-Net per card results
Figure 5: MLPerf Inference v2.0 RNNT per card results
Figure 6: MLPerf Inference v2.0 DLRM default and high accuracy per card results
Observations
The results in this blog have been officially submitted to and accepted by the MLCommons organization. These results have passed compliance tests, been peer reviewed, and adhered to the constraints enforced by MLCommons. Customers and partners can reproduce our results by following steps to run MLPerf Inference v2.0 in its GitHub repository.
Submissions from Dell Technologies included approximately 140 performance results and 28 performance and power results. Across the various workload tasks including Image Classification, Object Detection, Medical Image Segmentation, Speech-to-text, Language Processing, and Recommendation, server performance from Dell Technologies was promising.
Dell servers performed with optimal performance and power results. They were configured with different GPUs such as:
- NVIDIA A30 Tensor Core GPU
- NVIDIA A100 (PCIe and SXM)
- NVIDIA T4 Tensor Core GPU
- NVIDIA A2 Tensor Core GPU, which is newly released
More information about performance for specific configurations that are not discussed in this blog can be found in the v1.1 or v1.0 results.
The submission included results from different inference backends such as NVIDIA TensorRT and NVIDIA Triton. The appendix provides a summary of the full hardware and software stacks.
Conclusion
This blog quantifies the performance of Dell servers in the MLPerf Inference v2.0 round of submission. Readers can use these results to make informed planning and purchasing decisions for their AI workload needs.
Appendix
Software stack
The NVIDIA Triton Inference Server is an open-source inferencing software tool that aids in the deployment and execution of AI models at scale in production. Triton not only works with all major frameworks but also with customizable backends, further enabling developers to focus on their AI development. It is a versatile tool because it supports any inference type and can be deployed on any platform including CPU, GPU, data center, cloud, or edge. Additionally, Triton supports the rapid and reliable deployment of AI models at scale by integrating well with Kubernetes, Kubeflow, Prometheus, and Grafana. Triton supports the HTTP/REST and GRPC protocols that allow remote clients to request inferencing for any model that the server manages.
The NVIDIA TensorRT SDK delivers high-performance deep learning inference that includes an inference optimizer and runtime. It is powered by CUDA and offers a unified solution to deploy on various platforms including edge or data center. TensorRT supports the major frameworks including PyTorch, TensorFlow, ONNX, and MATLAB. It can import models trained in these frameworks by using integrated parsers. For inference, TensorRT performs orders of magnitude faster than its CPU-only counterparts.
NVIDIA MIG can partition GPUs into several instances that extend compute resources among users. MIG enables predictable performance and maximum GPU use by running jobs simultaneously on the different instances with dedicated resources for compute, memory, and memory bandwidth.
SUT configuration
The following table describes the SUT from this round of data center inference submission:
Table 1: MLPerf Inference v2.0 system configurations for DSS 8440 and PowerEdge R750xa servers
Platform | DSS 8440 10xA100 TensorRT | R750xa 4xA100 TensorRT |
MLPerf system ID | DSS8440_A100_PCIE_80GBx10_TRT | R750xa_A100_PCIE_80GBx4_TRT |
Operating system | CentOS 8.2 | |
CPU | Intel Xeon Gold 6248R CPU @ 3.00 GHz | Intel Xeon Gold 6338 CPU @ 2.00 GHz |
Memory | 768 GB | 1 TB |
GPU | NVIDIA A100 | |
GPU form factor | PCIe | |
GPU count | 10 | 4 |
Software stack | TensorRT 8.4.0 CUDA 11.6 cuDNN 8.3.2 Driver 510.39.01 DALI 0.31.0 |
Table 2: MLPerf Inference v2.0 system configurations for PowerEdge XE2420 servers
Platform | PowerEdge XE2420 1xA30 TensorRT | PowerEdge XE2420 2xA30 TensorRT | PowerEdge XE2420 1xA30 TensorRT MaxQ | PowerEdge XE2420 1xAT4 TensorRT |
MLPerf system ID | XE2420_A30x1_TRT | XE2420_A30x2_TRT | XE2420_A30x1_TRT_MaxQ | XE2420_T4x1_TRT |
Operating system | Ubuntu 20.04.4 | CentOS 8.2.2004 | ||
CPU | Intel Xeon Gold 6252 CPU @ 2.10 GHz | Intel Xeon Gold 6252N CPU @ 2.30 GHz | Intel Xeon Silver 4216 CPU @ 2.10 GHz | Intel Xeon Gold 6238 CPU @ 2.10 GHz |
Memory | 1 TB | 64 GB | 256 GB | |
GPU | NVIDIA A30 | NVIDIA T4 | ||
GPU form factor | PCIe | |||
GPU count | 1 | 2 | 1 | 1 |
Software stack | TensorRT 8.4.0 CUDA 11.6 cuDNN 8.3.2 Driver 510.39.01 DALI 0.31.0 |
Table 3: MLPerf Inference v2.0 system configurations for PowerEdge XE8545 servers
Platform | PowerEdge XE8545 4xA100 TensorRT | PowerEdge XE8545 4xA100 TensorRT, Triton | PowerEdge XE8545 1xA100 MIG 1x1g.10g TensorRT
|
MLPerf system ID | XE8545_A100_SXM_80GBx4_TRT | XE8545_A100_SXM_80GBx4_TRT_Triton | XE8545_A100_SXM_80GB_1xMIG_TRT |
Operating system | Ubuntu 20.04.3 | ||
CPU | AMD EPYC 7763 | ||
Memory | 1 TB | ||
GPU | NVIDIA A100-SXM-80 GB | NVIDIA A100-SXM-80 GB (1x1g.10gb MIG) | |
GPU form factor | SXM | ||
GPU count | 4 | 1 | |
Software stack | TensorRT 8.4.0 CUDA 11.6 CuDNN 8.3.2 Driver 510.47.03 DALI 0.31.0 | ||
| Triton 22.01 |
|
Table 4: MLPerf Inference v2.0 system configurations for PowerEdge XR12 servers
Platform | PowerEdge XR12 1xA2 TensorRT | PowerEdge XR12 1xA2 TensorRT MaxQ |
MLPerf system ID | XR12_A2x1_TRT | XR12_A2x1_TRT_MaxQ |
Operating system | CentOS 8.2 | |
CPU | Intel Xeon Gold 6312U CPU @ 2.40 GHz | |
Memory | 256 GB | |
GPU | NVIDIA A2 | |
GPU form factor | PCIe | |
GPU count | 1 | |
Software stack | TensorRT 8.4.0 CUDA 11.6 cuDNN 8.3.2 Driver 510.39.01 DALI 0.31.0 |

Choosing a PowerEdge Server and NVIDIA GPUs for AI Inference at the Edge
Fri, 05 May 2023 16:38:19 -0000
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Dell Technologies submitted several benchmark results for the latest MLCommonsTM 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 MLPerfTM 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:
MLCommonsTM Inference v3.0 results presented in this document are based on following system IDs:
ID | Submitter | Availability | System |
---|---|---|---|
2.1-0005 | Dell Technologies | Available | Dell PowerEdge XE2420 (1x T4, TensorRT) |
2.1-0017 | Dell Technologies | Available | Dell PowerEdge XR4520c (1x A2, TensorRT) |
2.1-0018 | Dell Technologies | Available | Dell PowerEdge XR4520c (1x A30, TensorRT) |
2.1-0019 | Dell Technologies | Available | Dell PowerEdge XR4520c (1x A2, MaxQ, TensorRT) |
2.1-0125 | Dell Technologies | Preview | Dell PowerEdge XR5610 (1x L4, TensorRT, MaxQ) |
2.1-0126 | Dell Technologies | Preview | Dell PowerEdge XR7620 (1x L4, TensorRT) |
Table 1: MLPerfTM system IDs