Supercharge Inference Performance at the Edge using the Dell EMC PowerEdge XE2420 (June 2021 revision)
Mon, 07 Jun 2021 13:42:14 -0000
|Read Time: 0 minutes
Deployment of compute at the Edge enables the real-time insights that inform competitive decision making. Application data is increasingly coming from outside the core data center (“the Edge”) and harnessing all that information requires compute capabilities outside the core data center. It is estimated that 75% of enterprise-generated data will be created and processed outside of a traditional data center or cloud by 2025.[1]
This blog demonstrates the high power-performance potential of the Dell EMC PowerEdge XE2420, an edge-friendly, short-depth server. Utilizing up to four NVIDIA T4 GPUs, the XE2420 can perform AI inference operations faster while efficiently managing power-draw. The XE2420 is capable of classifying images at 23,309 images/second while drawing an average of 794 watts, all while maintaining its equal performance with other conventional rack servers.
XE2420 Features and Capabilities
The Dell EMC PowerEdge XE2420 is a 16” (400mm) deep, high-performance server that is purpose-built for the Edge. The XE2420 has features that provide dense compute, simplified management and robust security for harsh edge environments.
- Built for performance: Powerful 2U, two-socket performance with the flexibility to add up to four accelerators per server and a maximum local storage of 132TB.
- Designed for harsh edge environments: Tested to Network Equipment-Building System (NEBS3) guidelines, with extended operating temperature tolerance of 5˚-45˚C, and an optional filtered bezel to guard against dust. Short depth for edge convenience and lower latency.
- Integrated security and consistent management: Robust, integrated security with cyber-resilient architecture, and the new iDRAC9 with Datacenter management experience. Front accessible and cold-aisle serviceable for easy maintenance.
- Power efficiency: High-end capacity supporting 2x 2000W AC PSUs or 2x 1100W DC PSUs to support demanding configurations, while maintaining efficient operation minimizing power draw
The XE2420 allows for flexibility in the type of GPUs you use in order to accelerate a wide variety of workloads including high-performance computing, deep learning training and inference, machine learning, data analytics, and graphics. It can support up to 2x NVIDIA V100/S PCIe, 2x NVIDIA RTX6000, or up to 4x NVIDIA T4.
Edge Inferencing with the T4 GPU
The NVIDIA T4 is optimized for mainstream computing environments and uniquely suited for Edge inferencing. Packaged in an energy-efficient 70-watt, small PCIe form factor, it features multi-precision Turing Tensor Cores and RT Cores to deliver power efficient inference performance. Combined with accelerated containerized software stacks from NGC, the XE2420 combined with NVIDIA T4s is a powerful solution to deploy AI application at scale on the edge.
Fig 1: NVIDIA T4 Specifications
Fig 2: Dell EMC PowerEdge XE2420 w/ 4x T4 & 2x 2.5” SSDs
Dell EMC PowerEdge XE2420 MLPerf™ Inference v1.0 Tested Configuration
Processors | 2x Intel Xeon Gold 6252 CPU @ 2.10GHz |
Storage
| 1x 2.5" SATA 250GB |
1x 2.5" NVMe 4TB | |
Memory | 12x 32GB 2666MT/s DDR4 DIMM |
GPUs | 4x NVIDIA T4 |
OS | Ubuntu 18.04.4 |
Software
| TensorRT 7.2.3 |
CUDA 11.1 | |
cuDNN 8.1.1 | |
Driver 460.32.03 | |
DALI 0.30.0 | |
Hardware Settings | ECC on |
Inference Use Cases at the Edge
As computing further extends to the Edge, higher performance and lower latency become vastly more important in order to increase throughput, while decreasing response time and power draw. One suite of diverse and useful inference workload benchmarks is the MLPerf™ suite from MLCommons™. MLPerf™ Inference demonstrates performance of a system under a variety of deployment scenarios, aiming to provide a test suite to enable balanced comparisons between competing systems along with reliable, reproducible results.
The MLPerf™ Inference v1.0 suite covers a variety of workloads, including image classification, object detection, natural language processing, speech-to-text, recommendation, and medical image segmentation. Specific datacenter scenarios covered include “offline”, which represents batch processing applications such as mass image classification on existing photos, and “Server”, which represents an application where query arrival is random, and latency is important. An example of server is any consumer-facing website where a consumer is waiting for an answer to a question. For MLPerf™ Inference v1.0, we also submitted using the edge scenario of “SingleStream”, representing an application that delivers single queries in a row, waiting to deliver the next only when the first is finished; latency is important to this scenario. One example of SingleStream is smartphone voice transcription: Each word is rendered as it spoken, and the second word does not render the next until the first is done. Many of these workloads are directly relevant to Telco & Retail customers, as well as other Edge use cases where AI is becoming more prevalent.
MLPerf™ Inference v1.0 now includes power benchmarking. This addition allows for measurement of power draw under active test for any of the benchmarks, which provide accurate and precise power metrics across a range of scenarios, and is accomplished by utilization of the proprietary measurement tool belonging to SPECPower – PTDaemon®. SPECPower is an industry-standard benchmark built to measure power and performance characteristics of single or multi-node compute servers. Dell EMC regularly submits PowerEdge systems to SPECPower to provide customers the data they need to effectively plan server deployment. The inclusion of comparable power benchmarking to MLPerf™ Inference further emphasizes Dell’s commitment to customer needs.
Measuring Inference Performance using MLPerf™
We demonstrate inference performance for the XE2420 + 4x NVIDIA T4 accelerators across the 6 benchmarks of MLPerf™ Inference v1.0 with Power v1.0 in order to showcase the workload versatility of the system. Dell tuned the XE2420 for best performance and measured power under that scenario to showcase the optimized NVIDIA T4 power cooling algorithms. The inference benchmarking was performed on:
- Offline, Server, and SingleStream scenarios at 99% accuracy for ResNet50 (image classification), RNNT (speech-to-text), and SSD-ResNet34 (object detection), including power
- Offline and Server scenarios at 99% and 99.9% for DLRM (recommendation), including power
- Offline and SingleStream scenario at 99% and 99.9% accuracy for 3D-Unet (medical image segmentation)
These results and the corresponding code are available at the MLPerf™ website. We have submitted results to both the Datacenter[2] & the Edge suites[3].
Key Highlights
At Dell, we understand that performance is critical, but customers do not want to compromise quality and reliability to achieve maximum performance. Customers can confidently deploy inference workloads and other software applications with efficient power usage while maintaining high performance, as demonstrated below.
The XE2420 is a compact server that supports 4x 70W NVIDIA T4 GPUs in an efficient manner, reducing overall power consumption without sacrificing performance. This high-density and efficient power-draw lends it increased performance-per-dollar, especially when it comes to a per-GPU performance basis.
Dell is a leader in the new addition of MLPerf™ Inference v1.0 Power measurements. Due to the leading-edge nature of the measurement, limited datasets are available for comparison. Dell also has power measurements for the core datacenter R7525, configured with 3x NVIDIA A100-PCIe-40GB. On a cost per throughput per watt comparison, XE2420 configured with 4x NVIDIA T4s gets better power performance in a smaller footprint and at a lower price, all factors that are important for an edge deployment.
Inference benchmarks tend to scale linearly within a server, as this type of workload does not require GPU P2P communication. However, the quality of the system can affect that scaling. The XE2420 showcases above-average scaling; 4 GPUs provide more than 4x performance increase! This demonstrates that operating capabilities and performance were not sacrificed to support 4 GPUs in a smaller depth and form-factor.
Dell submitted to the Edge benchmark suite of MLPerf™ Inference v1.0 for the third round of MLPerf Inference Testing. The unique scenario in this suite is “SingleStream”, discussed above. With SingleStream, system latency is paramount, as the server cannot move onto the second query until the first is finished. The fewer milliseconds, the faster the system, and the better suited it is for the Edge! System architecture affects latency, so depending on where the GPU is located latency may increase or decrease. This figure can be read as a best and worst case scenario; ie the XE2420 will return results on average in between 6.8 to 8.73 milliseconds, below the range of human-recongnizable delay for the SSD-ResNet34 benchmark. Not every server will meet this bar on every benchmark, and the XE2420 scores below this range on many of the submissions.
Comparisons to MLPerf™ Inference v0.7 XE2420 results will show that v1.0 results are slightly different in terms of total system and per-GPU throughput. This is due to a changed requirement between the two test suites. In v0.7, ECC could be turned off, which is common to improve performance of GDDR6 based GPUs. In v1.0, ECC is turned on. This better reflects most customer environments and use cases, since administrators will typically be alerted to any memory errors that could affect accuracy of results.
Conclusion: Better Performance-per-Dollar and Flexibility at the Edge without sacrificing Performance
MLPerf™ inference benchmark results clearly demonstrate that the XE2420 is truly a high-performance, efficient, half-depth server ideal for edge computing use cases and applications. The capability to support four NVIDIA T4 GPUs in a short-depth, edge-optimized form factor, while keeping them sufficiently cool enables customers to perform AI inference operations at the Edge on par with traditional mainstream 2U rack servers deployed in core data centers. The compact design provides customers new, powerful capabilities at the edge to do more even faster without extra cost or increased power requirements. The XE2420 is capable of true versatility at the edge, demonstrating strong performance not only for mundane workloads but also for a broad range of tested workloads, applicable in a number of Edge industries from Retail to Manufacturing to Autonomous driving. Dell EMC offers a complete portfolio of trusted technology solutions to aggregate, analyze and curate data from the edge to the core to the cloud and XE2420 is a key component of this portfolio to meet your compute needs at the Edge.
XE2420 MLPerf™ Inference v1.0 Full Results
The raw results from the MLPerf™ Inference v1.0 published benchmarks are displayed below, where the performance metric is throughput (items per second) for Offline and Server and latency (length of time to return a result, in milliseconds) for SingleStream. The power metric is Watts for Offline and Server and Energy (Joules) per Stream for SingleStream.
|
| 3d-unet-99 | 3d-unet-99.9 | ||
|
| Offline | SingleStream | Offline | SingleStream |
XE2420_T4x1_TRT | Performance | - | - | - | - |
Power/Energy | - | - | - | - | |
XE2420_T4x4_TRT | Performance | 31.22 (imgs/sec) | 171.73 (ms) | 31.22 (imgs/sec) | 171.73 (ms) |
Power/Energy | - | - | - | - | |
|
| dlrm-99.9 | dlrm-99 | ||
|
| Offline | Server | Offline | Server |
XE2420_T4x1_TRT | Performance | - | - | - | - |
Power/Energy | - | - | - | - | |
XE2420_T4x4_TRT | Performance | 135,149.00 (imgs/sec) | 126,531.00 (imgs/sec) | 135,189.00 (imgs/sec) | 126,531.00 (imgs/sec) |
Power/Energy | 829.09 (W) | 835.52 (W) | 830.13 (W) | 835.91 (W) | |
|
| resnet50 | ||
|
| Offline | Server | SingleStream |
XE2420_T4x1_TRT | Performance | 5,596.34 (imgs/sec) | - | 0.83 (ms) |
Power/Energy | - | - | - | |
XE2420_T4x4_TRT | Performance | 23,309.30 (imgs/sec) | 21,691.30 (imgs/sec) | 0.91 (ms) |
Power/Energy | 794.46 (W) | 792.69 (W) | 0.59 (Joules/Stream) | |
|
| rnnt | ||
|
| Offline | Server | SingleStream |
XE2420_T4x1_TRT | Performance | - | - | - |
Power/Energy | - | - | - | |
XE2420_T4x4_TRT | Performance | 5,704.60 (imgs/sec) | 4,202.02 (imgs/sec) | 71.75 (ms) |
Power/Energy | 856.80 (W) | 862.46 (W) | 31.77 (Joules/Stream) | |
|
| ssd-resnet34 | ||
|
| Offline | Server | SingleStream |
XE2420_T4x1_TRT | Performance | 129.28 (imgs/sec) | - | 8.73 (ms) |
Power/Energy | - | - | - | |
XE2420_T4x4_TRT | Performance | 557.43 (imgs/sec) | 500.96 (imgs/sec) | 6.80 (ms) |
Power/Energy | 792.85 (W) | 790.83 (W) | 4.81 (Joules/Stream) | |
Related Blog Posts
Can CPUs Effectively Run AI Applications?
Fri, 03 Mar 2023 20:06:28 -0000
|Read Time: 0 minutes
Due to the inherent advantages of GPUs in high speed scale matrix operations, developers have gravitated to GPUs for AI training (developing the model) and inference (the model in execution).
With the scarcity of GPUs driven by the massive growth of AI applications, including recent advancements in stable diffusion and large language models that have taken the world by storm, such as ChatGPT by OpenAI, the question for many developers is:
Are CPUs up to the task of AI?
To answer the question, Dell Technologies and Scalers AI set up a Dell PowerEdge R760 server with 4th Gen Intel® Xeon® processors and integrated Intel® Deep Learning acceleration. Notably, we did not install a GPU on this server.
In this blog, Part One of a two-part series, we’ll put this latest and greatest Intel® Xeon® CPU just released this month by Intel® to the test on AI inference . We’ll also run AI on video streams, one of the most common mediums to run AI, and pair industry specific application logic to showcase a real-world AI workload.
In Part Two, we’ll train a model in a technique called transfer learning. Most training is done on GPUs today, and transfer learning presents a great opportunity to leverage existing models while customizing for targeted use cases.
The industry specific use case
Scalers AI developed a smart city solution that uses artificial intelligence and computer vision to monitor traffic safety in real time. The solution identifies potential safety hazards, such as illegal lane changes on freeway on-ramps, reckless driving, and vehicle collisions, by analyzing video footage from cameras positioned at key locations.
For comparison, we also set up the previous generation Dell PowerEdge R750 server and ran the AI inferencing object detection workload on both servers. What did we learn?
Dell PowerEdge R760 with 4th Gen Intel® Xeon® Processors and Intel® Deep Learning Boost delivered!
Let’s find out about the generational server comparison.
The following charts show the performance gain from the last gen to the current gen server. The graph on the left shows inference-only performance, while the middle graph adds video decode. Finally, the graph on the right shows the full application performance with the smart city solution application logic.
The performance claims are great. But what does this mean for my business?
Dell PowerEdge R760 and Scalers AI smart city solution results show that for a similar application, users can expect the Dell PowerEdge R760 server to perform real-time inferencing on up to 90 1080P video streams when it is deployed. Dell PowerEdge R750 can handle up to 50 1080P video streams, and this is all without a GPU. Although GPUs add additional AI computing capability, this study shows that they may only sometimes be necessary, depending on your unique requirements, such as how many streams must be displayed concurrently.
Given these results, Scalers AI confidently recommends using Dell PowerEdge R760 with 4th Gen Intel® Xeon® Processors and Intel® Deep Learning Boost for AI computer vision workloads, such as the Scalers AI Traffic Safety Solution using object detection, because they fulfill all application requirements.
Now that we have shown highly effective object detection on a CPU, what about a more compute-intensive complex model such as segmentation?
Here we are running segmentation on 10 streams, while displaying four streams on the more complex segmentation model.
As you can see, CPUs are up to the task of running AI inference on models such as object detection and segmentation. Perhaps more important for developers, they offer the flexibility to run the full workload on the same processor, thereby lowering the TCO.
With the rapid growth of AI, the ability to deploy on CPUs is a key differentiator for real-world use cases such as traffic safety. This frees up GPU resources for training and graphics use cases.
Check in for Part Two of this blog series as we discuss a technique to train a transfer learning model and put a CPU to the test there.
Resources
Interested in trying for yourself? Get access to the solution code!
To save developers hundreds of potential hours of development, Dell Technologies and Scalers AI are offering access to the solution code to fast-track development of AI workloads on next-generation Dell PowerEdge servers with 4th Gen Intel® Xeon® scalable processors.
For access to the code, reach out to your Dell representative or contact Scalers AI!
To learn more about the study discussed here, visit the following webpages:
• Myth-Busting:
Can Intel® Xeon® Processors Effectively Run AI Applications?
• Accelerate Industry Transformation:
Build Custom Models with Transfer Learning on Intel® Xeon®
• Scalers AI Performance Insights:
Dell PowerEdge R760 with 4th Gen Intel® Xeon® Scalable Processors in AI
Authors:
Steen Graham, CEO at Scalers AI
Delmar Hernandez, Server Technologist at Dell Technologies
Interpreting TPCx-AI Benchmark Results
Wed, 01 Feb 2023 14:29:11 -0000
|Read Time: 0 minutes
TPCx-AI Benchmark
Overview
TPCx-AI Benchmark abstracts the diversity of operations in a retail data center scenario. Selecting a retail business model assists the reader relate intuitively to the components of the benchmark, without tracking that industry segment tightly. Such tracking would minimize the relevance of the benchmark. The TPCx-AI benchmark can be used to characterize any industry that must transform operational and external data into business intelligence.
This paper introduces the TPCx-AI benchmark and uses a published TPCx-AI result to describe how the primary metrics are determined and how they should be read.
Benchmark model
TPCx-AI data science pipeline
The TPCx-AI benchmark imitates the activity of retail businesses and data centers with:
- Customer information
- Department stores
- Sales
- Financial data
- Product catalog and reviews
- Emails
- Data center logs
- Facial images
- Audio conversations
It models the challenges of end-to-end artificial intelligence systems and pipelines where the power of machine learning and deep learning is used to:
- Detect anomalies (fraud and failures)
- Drive AI-based logistics optimizations to reduce costs through real-time forecasts (classification, clustering, forecasting, and prediction)
- Use deep learning AI techniques for customer service management and personalized marketing (facial recognition and speech recognition)
It consists of ten different use cases that help any retail business data center address and manage any business analysis environment.
The TPCx-AI kit uses a Parallel Data Generator Framework (PDGF) to generate the test dataset. To mimic the datasets of different company sizes the user can specify scale factor (SF), a configuration parameter. It sets the target input dataset size in GB. For example, SF=100 equals 100 GB. Once generated, all the data is processed for subsequent stages of postprocessing within the data science pipeline.
Use cases
The TPCx-AI Benchmark models the following use cases:
Figure 1: TPCx-AI benchmark use case pipeline flow
Table 1: TPCx-AI benchmark use cases
ID | Use case | M/DL | Area | Algorithm |
UC01 | Customer Segmentation | ML | Analytics | K-Means |
UC02 | Conversation Transcription | DL | NLP | Recurrent Neural Network |
UC03 | Sales Forecasting | ML | Analytics | ARIMA |
UC04 | Spam Detection | ML | Analytics | Naïve Bayes |
UC05 | Price Prediction | DL | NLP | RNN |
UC06 | Hardware Failure Detection | ML | Analytics | Support Vector Machines |
UC07 | Product Rating | ML | Recommendation | Alternating Least Squares |
UC08 | Trip Type Classification | ML | Analytics | XGBoost |
UC09 | Facial Recognition | DL | Analytics | Logistic Regression |
UC10 | Fraud Detection | ML | Analytics | Logistic Regression |
Benchmark run
The TPCx-AI Benchmark run consists of seven separate tests run sequentially. The tests are listed below:
- Data Generation using PDGF
- Load Test – Loads data into persistent storage (HDFS or other file systems)
- Power Training Test – Generates and trains models
- Power Serving Test I – Uses the trained model in Training Phase to conduct the serving phase (Inference) for each use case
- Power Serving Test II – There are two serving tests that run sequentially. The test with the greater geometric mean (geomean) of serving times is used in the overall score.
- Scoring Test – Model validation stage. Accuracy of the model is determined using defined accuracy metrics and criteria
- Throughput Test – Runs two or more concurrent serving streams
The elapsed time for each test is reported.
Note: There are seven benchmark phases that span an end-to-end data science pipeline as shown in Figure 1. For a compliant performance run, the data generation phase is run but not scored and consists of the subsequent six separate tests, load test through throughput test, run sequentially.
Primary metrics
For every result, the TPC requires the publication of three primary metrics:
- Performance
- Price-Performance
- Availability Date
Performance metric
It is possible that not all scenarios in TPCx-AI will be applicable to all users. To account for this situation, while defining the performance metric for TPCx-AI, no single scenario dominates the performance metric. The primary performance metric is the throughput expressed in terms of AI use cases per minute (AIUCpm) @ SF is defined in the figure below.
Figure 2: Definition of the TPCx-AI benchmark metric
Where:
TLD = Load time
TPTT = Geomean of training times
TPST1 = Geomean of Serving times
TPST2 = Geomean of serving times
TPST = Max (TPST1, TPST2)
TTT = Total elapsed time/ (#streams * number of use cases)
N = Number of use cases
Note: The elapsed time for the scoring test is not considered for the calculation of the performance metric. Instead, the results of the scoring test are used to determine whether the Performance test was successful.
The scoring test result for each user case should meet or better the reference result set provided in the kit as shown in the figure below.
Figure 3: Benchmark run accuracy metrics
Calculating the Performance metric
To illustrate how the performance metric is calculated, let us consider the results published for SF=10 at:
https://www.tpc.org/tpcx-ai/results/tpcxai_result_detail5.asp?id=122110802
A portion of the TPCx-AI result highlights, showing the elapsed time for the six sequential tests constituting the benchmark run is shown in the figure below.
Figure 4: Elapsed time for the benchmark test phases
The result highlights only provide the training times and the serving times. To calculate the final performance metric, we need to use the geometric mean of the training times and serving times. To arrive at the geomean of the training times and the testing times, the time taken for each use case is needed. That time is provided in the Full Disclosure Report (FDR) that is part of the benchmark results. The link to the FDR of the SF=10 results that we are considering are at:
https://www.tpc.org/results/fdr/tpcxai/dell~tpcxai~10~dell_poweredge_r7615~fdr~2022-11-09~v01.pdf
The use case times and accuracy table from the FDR are shown in the figure below.
Figure 5: Use case times and accuracy
Note: The accuracy metrics are defined in Table 7a of the TPCx-AI User Guide.
Using the data in Figure 4 and Figure 5:
TLD = Load time =2.306 seconds
TPTT = Geomean of training time =316.799337
(119.995*2104.383*113.122*89.595*974.454*424.76*26.14*4928.427*29.112*253.63)1/10
TPST1 = Geomean of Serving times =19.751 seconds
(10.025*8.949*4.405*12.05*4.489*144.016*4.254*396.486*75.706*22.987)1/10
TPST2 = Geomean of serving times = 19.893 seconds
(10.043*8.92*4.39*12.288*4.622*148.551*4.275*396.099*75.508*22.881)1/0
TPST = Max (TPST1, TPST2)= 19.893 seconds
TTT = Total elapsed time/ (#streams * # of use cases) =2748.071/ (100*10)= 2.748 seconds
N = Number of use cases =10
Note: The geometric mean is arrived at by multiplying the time taken for each of the use cases and finding the 10th root of the product.
Plugging the values in the formula for calculating the AIUCpm@SF given in Figure 2, we get:
AIUCpm@SF= 10*10*60/ (2.306*316.799*19.893*2.748)1/4
= 6000/ (39935.591)1/4
= 6000/14.1365=424.433
The actual AIUCpm@SF10=425.31
Calculating the Price-Performance metric
The Price-Performance metric is defined in the figure below.
Figure 6: Price-Performance metric definition
Where:
- P = is the price of the hardware and software components in the System Under Test (SUT)
- AIUCpm@SF is the reported primary performance metric
Note: A one-year pricing model must be used to calculate the price and the price-performance result of the TPCx-AI Benchmark.
AIUCpm@SF10 = 425.31
Price of the configuration =$ 48412
$/AIUCpm@SF10 = 113.83 USD per AIUCpm@SF10
Availability date
All components used in this result will be orderable and available for shipping by February 22, 2023.
Performance results
Dell has published six world record-setting results based on the TPCx-AI Benchmark standard of the TPC. Links to the publications are provided below.
SF1000
Dell PowerEdge R650/Intel Xeon Gold (Ice Lake) 6348/CDP 7.1.7—11 nodes
https://www.tpc.org/tpcx-ai/results/tpcxai_result_detail5.asp?id=122120101
SF300
Dell PowerEdge R6625/AMD EPYC Genoa 9354/CDP 7.1.7—four nodes
https://www.tpc.org/tpcx-ai/results/tpcxai_result_detail5.asp?id=122110805
SF100
Dell PowerEdge R6625/AMD EPYC Genoa 9354/CDP 7.1.7—four nodes
https://www.tpc.org/tpcx-ai/results/tpcxai_result_detail5.asp?id=122110804
SF30
Dell PowerEdge R6625/AMD EPYC Genoa 9174F/Anaconda3—one node
https://www.tpc.org/tpcx-ai/results/tpcxai_result_detail5.asp?id=122110803
SF10
Dell PowerEdge R7615/AMD EPYC Genoa 9374F/Anaconda3—one node
https://www.tpc.org/tpcx-ai/results/tpcxai_result_detail5.asp?id=122110802
SF3
Dell PowerEdge R7615/AMD EPYC Genoa 9374F/Anaconda3—one node
https://www.tpc.org/tpcx-ai/results/tpcxai_result_detail5.asp?id=122110801
With these results, Dell Technologies holds the following world records on the TPCx-AI Benchmark Standard:
- #1 Performance and Price-Performance on SF1000
- #1 Performance and Price-Performance on SF300
- #1 Performance and Price-Performance on SF100
- #1 Performance and Price-Performance on SF30
- #1 Performance on SF10
- #1 Performance Price-Performance on SF3
Conclusion
Summary
This blog describes the TPCx-AI benchmark and how the performance result of the TPCx-AI Benchmark can be interpreted. It also describes how Dell Technologies maintains leadership in the TPCx-AI landscape.