The X11 Supermicro Data Center Engineered Supersaver® line provides optimal performance per watt and dollar to today’s data centers. This product line is a complex requirement with an improved thermal architecture with energy-efficient elements, staggered CPU placement to remove CPU thermal residues, and maximum power supply to allow higher operating temperatures. Fully adapted to and complement energy-efficient data center strategies to reduce total costs Profit of ownership. The 

Supermicro’s professional server design, good components, and support for the latest third-party Intel® Xeon® climbable CPUs give you best results in your data facility deployment. Every server provides up to 2TB of DDR4 2933MHz ECC remembrance with 1 PCI-E 3.0 slot, 8 DIMM slots, 8 SATA 3 energy anchorages (6Gbps), and supports Intel® C620 regulator and up to 2x1GbE.

The maximum number supported is: 

  • Each socket has 24 cores. 
  • memory: 2TB DDR42 933MHz ECC 
  • per socket, 140W 444 41 slots PCIE3.0x8 (FL) 
  • High-efficiency 800W power supply with redundancy

Embedded Super Servers in Rackmount

The rack-mount system is equipped with a powerful processor and is highly customizable. Supermicro Embedded Super Servers are application-optimized, cost-effective and space-constrained. The designed plan for various CPUs, including Intel® Atom® and Intel Xeon®, are flexible. Its multiple form factors are ideal for IoT or embedded applications. It has a competitive price/performance ratio, reliability, and low power consumption.

Solutions for deep learning techniques from AI deep learning and Supermicro

Deep Learning is part of Artificial Intelligence (AI) & Machine Learning (ML) that uses the multi-layered synthetic neural community to make troubles that can be too hard to design. Every day, for instance, Google Maps analyses vast quantities of statistics to decide the premiere course to take or assume the time it’ll take to get to the preferred location. There are factors to Deep Learning: education and inference. Deep Learning’s education segment includes studying as many statistics factors so as for the neural community to ‘learn’ the capabilities of its personnel and alternate itself to carry out responsibilities consisting of picture reputation and audio reputation. The act of locating a skilled classifier and using it to generate valuable predictions and judgments is known as inference. Each mastering and inferencing is required in vast portions of computational power to obtain the considered necessary accuracy and precision.

Advantages of Deep Learning work station as a Computational Powerhouse 

Supermicro super Server® computers are high-density and compact processing powerhouses that power the Supermicro AI & Deep Learning cluster. NVIDIA, a Supermicro partner, provided the newest GPUs for the group. Deep Learning uses NVIDIA® Tesla® V100 GPUs in each computation node. The advantages include;

  • Parallel Computing with a High Density 

 For optimum parallel computing performance and decreased training phase for Deep Learning tasks, up to 32 GPUs with 1TB of System memory are available. 

  • NVLink provides increased bandwidth. 

 Uses NVLinkTM, which improves system performance by allowing quicker GPU connection under intense Deep Learning workloads. 

  • Tensor Core allows for faster processing. 

 NVIDIA Tesla V100 GPUs use the Neural Core architecture. Tensor cores support deep Learning, which can give 125 Vector TFLOPS for learning and inference workloads. 

  • Design That Scales 

 Scale-out design with 100G IB EDR fabric is exceptionally scalable for future expansion. Quick Flash Xtreme (RFX) — All-flash NVMe storage with high performance. A comprehensive RFX storage system, including the Supermicro Big TwinTM and the parallel filing system WekaIO, has been developed and thoroughly tested for AI deep Learning.


Cloud computing and data centers 

For TCO and PUE to below, differentiated servers are built on Resource Saving Architecture to enable cloud data centers, virtualized 5G core multi-node twin architectures and high-density blade systems. The best GPU server for machine learning / AI training is in the data center cations.

 Edge and virtualized RAN 

High-performance FPGA acceleration servers for virtual RAN (vRAN), including ORANEdge AI inference with GPU card options, can be built using 3rd generation Intel® Xeon® ascendable mainframes and time-sensitive applications. The Central Office and Small Data Center features include NEBS Level 3 compatibility, AC / DC power supply, front panel I / O and bare servers, and a rugged IP65 enclosure for outdoor use.

IoT and customer location 

Compact gateway server and gateway for cloud-based management of IoT device installations are networking solutions that are easy to reconfigure for virtually any installation. Software partners offer a wide range of VNFs for SDWAN, uCPE, and other solutions.