SOLUTION BRIEF

SupermicroX13 BigTwin Multi-Node Infrastructure Solutions

Executive Summary
TABLE OF CONTENTS
Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Value of vSAN & AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Supermicro X13 BigTwin for Efficiency and Performance . . . . . 2
AI Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

With the newfound popularity of AI solutions such as ChatGPT,
enterprises are more motivated than ever to deploy AI use cases.
By tapping into their rich data sources, enterprises seek todeliver deep real-time insights that improve operational
efficiencies, lead to better product designs, improve customer
satisfaction and employee productivity, and ultimately increase
revenue streams.

Supermicro BigTwin Systems Details . . . . . . . . . . . . . . . . . . . . . . . . . 4
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Challenges:

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Successful AI solutions begin with data and end with insights.
Data is growing unchecked in both consumer segments andbusiness segments. Consumers have increasing tools to create and share information, leading to data growth numbers that
can hardly be comprehended. Businesses are doing their fair share to add to this mountain of data, hoping it can be leveraged
to provide massive value as business intelligence. Even though data is in high supply, while insights can be elusive, that is notdeterring enterprises from investing in AI. Worldwide spending on AI is expected to exceed $300 Billion by 2026. (According to
IDC’s Worldwide Artificial Intelligence Spending Guide, August 2022). As businesses look to cash in on the promise of AI, theymust improve data management or how data is collected, classified, and secured so that decisions and actions can be taken in
the organization's best interest.

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The integration of AI and VMware vSAN is in its infancy. Developers and data practitioners are currently exploring how
enterprises can use software-defined storage to utilize the valuable data stored in vSAN clusters for AI use cases. With amyriad of applications running in vSAN and the pervasive nature of AI, it's only a matter of time before advancements in vSAN,
including greater scalability, improved performance, and better application management, combine with new AI tools andcapabilities embedded in applications to offer the insights and innovation enterprises seek.

Value of vSAN & AI:
VMware vSAN provides a simple path to hyper-converged infrastructure (HCI)$300+ Billion
Global Spending on AI by 2026

Compute Platform• CPU is useful for inference, fine-tuning models, and training smaller models• Application architectures deploy multiple instances of the application to scale as needed.
• Multi-threaded processes offer high levels of resource utilization (CPU, network, and memory)
Data Platform• Storage clusters for large amounts of structured and unstructured data
• Backing hardware resources can be scaled as neededthIntel Advanced Matrix Extensions (Intel AMX) accelerates AI capabilities on 4 Gen Intel Xeon Scalable
processors, speeding up deep learning training and inferencing without additional hardware. It is ideal for NLP,
recommendation systems, and image classification and is supported out of the box in the most popular AI frameworks
such as TensorFlow, PyTorch, and OpenVINO™

Supermicro X13 BigTwin for Efficiency and Performance
Investing in the Supermicro X13 BigTwin for Artificial Intelligence (AI) workloads is a strategic decision that can transform abusiness's operations. With its density, customers get the latest and greatest processing power of multiple nodes working
simultaneously or individually to accelerate complex AI tasks like Natural Language Processing (NLP) and ImageClassification. With the Supermicro X13 BigTwin, customers are unlocking possibilities to drive businesses to new heights.
The flexible options of 2U 4-Node and 2U 2-Node that the Supermicro X13 BigTwin offers enable enterprises to choose themost efficient configuration for their business needs. With its density, AI workloads demand immense computational
resources, and the Supermicro X13 BigTwin delivers the computational power needed. By distributing the workloads acrossnodes, organizations can achieve remarkable speedups, reducing processing times from days to hours. This configurationmeans quicker insights, faster iterations, and a competitive edge in many industries. With the Supermicro X13 BigTwin power
efficiency, by sharing resources in a single chassis, ensures that AI models reach their full potential while maximizing thereturn on the system investment.
As organizations’ AI initiatives expand, teams won’t face the headache of outgrowing their infrastructure, as this has theflexibility and scalability that AI needs. Adding nodes is a seamless process that adapts to an organizations evolving needs.
Whether interested in deep learning, neural networks, or big data analysis, Supermicro X13 BigTwin future-proofs operations,allowing teams to tackle even the most demanding AI workloads.

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Supermicro X13 BigTwin Systems

Highlights:• Dual Socket 4th Gen Intel Xeon Scalable Processors Per Node
• Up to 4 Nodes per 2U• 16 DIMM Slots per Node, Up to 4TB DDR5-4800 Memory• Flexible Storage Options Including ALL Nvme and Hybrid Nvme/SAS3/SATA3 in 2.5” or 3.5”
• Flexible Networking Options from 1GBps to 400 Gbps
Innovations• Supermicro X13 BigTwin flexible design with 2U 2-Node or 2U 4-Node• Supermicro AIOM – Most Flexible, Cost-Optimized Server I/O• Resource Sharing for Best Efficiency and TCOIntel AMX supports two data types, INT8 and BF16, for the matrix multiplication required for AI workloads:• INT8 is a data type used for inferencing when the precision of FP32, a single-precision floating-point format often
used in AI, isn’t needed. Because the INT8 data type is lower precision, more INT8 operations can be processed percompute cycle.
• BF16 is a data type that delivers sufficient accuracy for most training. It can also deliver higher accuracy for
inferencing if needed.

AI Benchmarks
Image Classification – ResNet50
This benchmark has become a standard benchmark in the deep learning community for evaluating the performance of image
classification models. The Supermicro X13 BigTwin is powered by Intel 4th Gen Xeon Scalable Processors with AMX accelerators
brings TCO benefits for certain AI workloads. Enterprises can make informed decisions, increase efficiency, and provide
innovative solutions for their customers.

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Up to 4.9x Faster
Image Classification

10,000

Images/Second/Node

Image Classification on TensorFlow 2.11 Using ResNet50
Batch Size = 128, Multi-Instance (4 cores per Instance)
Higher is Better

9,385
3.1x Higher

when using a 4th Gen Intel® Xeon® Scalable
Processor with Intel ® AMX for BF16

8,000
6,000

Gen-over-Gen

4943

4,000

1.33x
Gen-Over-Gen

2,000

1002

749

4.9x Higher

2,943

with virtuallyno accuracy loss
vs AVX-512 FP32

0Intel AVX-512 for Intel AVX-512 for Intel AMX for
FP32
FP32
BF16
Intel Xeon Gold Intel Xeon Gold Intel Xeon Gold
6348 processor 6448Y processor 6448Y processor
2.6GHz, 28 cores 2.1 GHz, 32 cores 2.1 GHz, 32 coresIntel VNNI forIntel AMX for
INT8
INT8
Intel Xeon Gold Intel Xeon Gold
6348 processor 6448Y processor
2.6GHz, 28 cores 2.1GHz, 32 cores

Cost per 1M images - 1h time frame

Figure 1 – Image Classification Performance Compared to the Previous Generation

$0.70

$0.66

Image Classification on TensorFlow 2.11 Using ResNet50
Batch Size = 128, Multi-Instance (4 cores per Instance)
Cost Reduction 3y CAPEX - Lower is Better

13% less
Gen-Over-Gen

$0.57

$0.60
$0.50
$0.40
$0.30

$0.20

$0.12

$0.10

82% less$0.17

63% less
Gen-Over-Gen$0.06

with virtuallyno accuracy loss

$0.00
Intel AVX-512 for
FP32Intel Xeon Gold
6348 processor
2.6GHz, 28 coresIntel AVX-512 forIntel AMX for BF16
FP32
Intel Xeon GoldIntel Xeon Gold
6448Y processor 2.1
6448Y processor 2.1
GHz, 32 coresGHz,
32 coresIntel VNNI for INT8Intel Xeon Gold
6348 processor
2.6GHz, 28 coresIntel AMX for INT8Intel Xeon Gold
6448Y processor
2.1GHz, 32 cores

Figure 2 – CAPEX Calculation of ResNet50 Compared to the Previous Generation

Natural Language Processing – BERT-Large
This benchmark holds significant importance due to its pivotal role in evaluating and advancing language understanding
models. BERT Large is derived from the BERT model capable of handling more complex language tasks and larger datasets. It
can help enterprises draw insights for large and various data sources to provide personalized experiences that help increase© 2023 Copyright Super Micro Computer, Inc. All rights reserved

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user engagement. The Supermicro X13 BigTwin with up to 4-Node density in a 2U form factor is well-suited for running large
and complex models with the help of the Intel AMX accelerator.

Up to 4x Faster NLP

Samples/Second/Node150

when using a 4th Gen Intel® Xeon® Scalable
Processor with Intel ® AMX for BF16

120

105

90

4x Higher

1.23x

60
30

NLP on TensorFlow 2.11 Using BERT-Large
Batch Size = 128, 28x2 and 32x2 Instances
Higher is Better150

3.2x Higher
Gen-over-Gen

with virtuallyno accuracy loss

46

Gen-Over-Gen

26

21

0

Intel AVX-512 for
FP32Intel Xeon Gold
6348 processor
2.6GHz, 28 coresIntel AVX-512 for Intel AMX for BF16
FP32
Intel Xeon GoldIntel Xeon Gold
6448Y processor 2.1
6448Y processor 2.1
GHz, 32 cores
GHz, 32 coresIntel VNNI for INT8Intel Xeon Gold
6348 processor
2.6GHz, 28 coresIntel AMX for INT8Intel Xeon Gold
6448Y processor
2.1GHz, 32 cores

Figure 3 – Natural Language Processing Performance Compared to Previous Generation

NLP on TensorFlow 2.11 Using BERT-Large
Batch Size = 128, 28x2 and 32x2 Instances
Cost Reduction 3y CAPEX - Lower is Better

Cost per 1 sample - 1h time frame

7% less

0.45

$0.41

0.40

Gen-Over-Gen

$0.38

0.35
0.30
0.25

$0.19

0.20
0.15

Gen-Over-Gen

$0.09

0.10

$0.07

77% less

0.05

64% less

with virtuallyno accuracy loss

0.00
Intel AVX-512 for
FP32Intel Xeon Gold
6348 processor
2.6GHz, 28 coresIntel AVX-512 for Intel AMX for BF16
FP32
Intel Xeon GoldIntel Xeon Gold 6448Y processor 2.1
6448Y processor 2.1
GHz, 32 cores
GHz, 32 coresIntel VNNI for INT8 Intel AMX for INT8Intel Xeon GoldIntel Xeon Gold
6348 processor
6448Y processor
2.6GHz, 28 cores
2.1GHz, 32 cores

Figure 4 – CAPEX Calculation of BERT-Large Compared to Previous Generations
Configurations:
BASELINE: Intel Xeon Gold 6348 (ICX Config): 4-node cluster, Each node: 2x Intel® Xeon® Gold 6348 Processor, 1x Server Board M50CYP2UR, Total
Memory 512 GB (16x 32GB DDR4 3200MHz), HyperThreading: Enable, Turbo: Enabled, NUMA noSNC, Intel VMD: Enabled, BIOS:
SE5C620.86B.01.01.0008.2305172341(ucode:0xd000390), Storage (boot): 2x 80 GB Intel SSD P1600X, Storage (flat): 9x 3.84 TB Intel SSD DC P5510 Series
PCIe NVMe , Network devices: 1x Intel Ethernet E810CQDA2 E810-CQDA2, at 100 GbE RoCE, Network speed: 100 GbE, OS/Software: VMware/vSAN 8.0U1,
21495797, Test by Intel as of 07/04/2023 using Ubuntu Server 22.04 VM (vHW=20, vmxnet3), vSAN ESA – Optimal default policy (RAID-5), Kernel 5.15,© 2023 Copyright Super Micro Computer, Inc. All rights reserved

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intel-optimized-tensorflow:2.11.0, ResNet50v1.5, Batch size=128, VM=56vCPU+64GBRAM, Multi instance scenario (4 cores per instance), BERT-Large,
SQuAD 1.1, Batch size=128, VM=56vCPU+64GBRAMIntel Xeon Gold 6448Y (4th Gen Config): 4-node, 2x Intel® Xeon® Gold 6448Y, 4x SYS-221BT-DNTR X13DET-B, Total Memory 1024 GB (16x DDR5
64GB 4800MHz), HyperThreading: Enable, Turbo: Enabled, NUMA noSNC, Intel VMD: Enabled, BIOS: 1.3(ucode:0x2b000461), Storage (flat): 7x 6.4 TB
Solidigim P5620 Series PCIe NVMe , Network devices: 1x AOC-S100GC-I2C (Intel E810-CAM2), at 100 GbE RoCE, Network speed: 100 GbE, OS/Software:
VMware 8.0U1, 21495797 (vSAN in ESA mode), Test by Intel as of 6/23/2023 using Ubuntu Server 22.04 VM (vHW=20, vmxnet3), vSAN ESA – Optimal
default policy (RAID-5), Kernel 5.15, intel-optimized-tensorflow:2.11.0, ResNet50v1.5, Batch size=128, VM=64vCPU+64GBRAM, Multi instance scenario (4cores per instance), BERT-Large, SQuAD 1.1, Batch size=128, VM=64vCPU+64GBRAM

Conclusion
The convergence of AI solutions and enterprise aspirations has propelled the drive toward AI development. The exponential
growth of data both in customer and business realms, presents challenges, but enterprises remain with the need for more AI
solutions, evidenced by the substantial investments in AI. As the world anticipates AI spending to surpass $300 Billion by 2026,
the critical role of effective data management becomes paramount and Supermicro X13 BigTwin powered by 4th Gen Xeon
Scalable Processors can bring added value for this AI investments.
The integration of AI with Vmware vSAN is still in its early stages, with developers and data practitioners exploring ways toharness the potential of software-defined storage within the vSAN cluster for AI applications. The synergy between vSAN
advancements and emerging AI capabilities promises to deliver the required insights and innovation to enlightened enterprises.
Vmware vSAN’s role as a pathway to hyper-converged infrastructure cannot be understated. Its compute and data platforms
provide the necessary foundation for AI operations, offering scalable and efficient resources for various AI workloads. The
Supermicro X13 BigTwin stands out as a strategic investment for AI workloads, providing the processing power and flexibility
needed for complex tasks like Natural Language Processing and Image Classification which are used within inferencing, finetuning models, and training smaller models. The configuration options of the Supermicro X13 BigTwin, coupled with its power
efficiency and scalability, ensure that organizations can meet the demands of expanding AI initiatives without outgrowing theirinfrastructure.

References
To learn more about VMware-vSAN solutions of Supermicro, visit•

Supermicro Vmware-vSAN solutions

To learn more about Supermicro Server Infrastructure options, please visit:
•
•
•
•
•

Supermicro Data Center Server, Blade, Data Storage, AI System
Twin Servers: High-Density Multi-Node Server Solutions | Supermicro
Networking Devices & Hardware Products For HPC | SupermicroSupermicro Solution for Red Hat® OpenStack | Supermicro
AI Infrastructure Solutions | Supermicro

Sources
1)
2)

IDC Worldwide Artificial Intelligence Spending Guide, August 2022
Grand View Research, “Artificial Intelligence Market Size, Share & Trends Analysis Report By Solution, By Technology(Deep Learning, Machine Learning, Natural )”© 2023 Copyright Super Micro Computer, Inc. All rights reserved

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