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Cybertron International Inc.
@2020 CYBERTRON INTERNATIONAL INC. Wichita, KS.
All Rights Reserved.
@2020 CYBERTRON INTERNATIONAL INC. Wichita, KS.
All Rights Reserved.
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TAKE A MAJOR LEAP FORWARD
New Intel® Xeon® Scalable processors are workload-optimized to support hybrid cloud infrastructures and the most high-demand applications. You can drive actionable insight, count on hardware-based security, and deploy dynamic service delivery.
Optimize
Performance
New features such as Intel® Advanced Vector Extension 512 (Intel® AVX-512) improve with workload-optimized performance and throughput increases for advanced analytics, high performance computing (HPC) applications, and data compression.
Accelerate
Critical Workloads
Speed up data compression and cryptography with integrated Intel® QuickAssist Technology (Intel® QAT).
Operate
More Efficiently
High-speed Integrated Intel® Ethernet (up to 4x10GbE) helps reduce total system cost. It also lowers power consumption and improves transfer latency of large storage blocks and virtual machine migration.
Improve
Security
Deploy hardware-enhanced security to protect data and system operations without compromising performance.
Turn Data into Decisions—Faster Than Ever
Up to
5x
Analytics (4S database vs. installed base)¹Up to
2.2x
AI/deep learning training (vs. prior generation)²Up to
65%
Lower total cost of ownership (TCO)³
Intel® Xeon® Platinum processors offer the industry’s best performance for mission-critical and hybrid cloud workloads, real-time analytics, machine learning, and artificial intelligence, with monumental leaps in I/O, memory, storage, and network technologies.
CPU cores: Up to 28
Socket configurations: 8+
Memory: Up to 12 TB
Intel® Xeon® Gold processors offer high performance, advanced reliability, and hard-ware-enhanced security optimized for demand-ing data center, hybrid-cloud compute, network, and storage workloads
CPU cores: Up to 22
Socket configurations: Up to 4
Memory: Up to 6 TB
Intel® Xeon® Silver processors the hardware-enhanced performace and security required for data center compute, network and storage optimized for midsized and growing IT organizations.
CPU cores: Up to 12
Socket configurations: Up to 2
Memory: Up to 1.5 TB
Intel® Xeon® Bronze processors are perfor-mance optimized for small business and basic storage servers.
CPU cores: Up to 8
Socket configurations: Up to 2
Memory: Up to 1.5 TB
Scale for Success Push your business further and faster, with solutions based on Intel® Xeon® Scalable processors.
Cloud Services—Deliver Agility and Scale
Manage compute-hungry, data-rich workloads including Internet of Things (IoT), artificial intelligence, and visual computing with a leap in performance, data center design, and capabilities.
Communications Services—5G Ready
Capitalize on cloud economics with flexible, optimized industry-standard servers, and virtualized, orchestrated network services.
High-Performance Computing—Fuel Your Insights
Manage large, complex data sets, gain insights more quickly, accelerate product innovation, and drive scientific explorations that were never before possible.
Artificial Intelligence—Optimized for Deep Learning
Scale up quickly and seamlessly for 2.1X deep learning performance over previous generations, maximum flexibility with AI and combination workloads, and server-class reliability. LEARN MORE>
See How
It’s Done
Independent software vendors (ISV) are already putting Intel® Xeon® Scalable processors to work. Read use cases from across industries and technology segments.
Product and Performance Information
1Up to 5x claim based on OLTP warehouse workload: 1-Node, 4 x Intel® Xeon® processor E7-4870 on Emerald Ridge with 512 GB total memory on Oracle Linux* 6.4 using Oracle 12c* running 800 warehouses. Benchmark: HammerDB, Score: 2.46322e+006 higher is better vs. 1-Node, 4 x Intel® Xeon® Platinum 8180 processor on Lightning Ridge SKX with 768 GB total memory on Red Hat Enterprise Linux* 7.3 using Oracle* 12.2.0.1 (including database and grid) with 800 warehouses. Score: 1.2423e+007.
2Intel® Xeon® Platinum 8180 processor compared to Intel® Xeon® processor E5-2699 v4 NOTE: 113x gain in last 2 years, using optimized frameworks & optimized Intel® MKL Libraries compared to Intel® Xeon® processor E5-2699 v3 with BVLC-Caffe Platform: 2S Intel® Xeon® Platinum 8180 CPU 2.50GHz (28 cores), HT disabled, turbo disabled, scaling governor set to “performance” via intel_pstate driver, 384GB DDR4-2666 ECC RAM. CentOS* Linux release 7.3.1611 (Core), Linux kernel 3.10.0-514.10.2.el7.x86_64. SSD: Intel® SSD DC S3700 Series (800GB, 2.5in SATA 6Gb/s, 25nm, MLC). Performance measured with: Environment variables: KMP_AFFINITY='granularity=fine, compact‘, OMP_NUM_THREADS=56, CPU Freq set with cpupower frequency-set -d 2.5G -u 3.8G -g performance Deep Learning Frameworks: Caffe*: (http://github.com/intel/caffe/), revision f96b759f71b2281835f690af267158b82b150b5c. Inference measured with “caffe time --forward_only” command, training measured with “caffe time” command. For “ConvNet” topologies, dummy dataset was used. For other topologies, data was stored on local storage and cached in memory before training. Topology specs from https://github.com/intel/caffe/tree/master/models/intel_optimized_models (GoogLeNet, AlexNet, and ResNet-50), https://github.com/intel/caffe/tree/master/models/default_vgg_19 (VGG-19), and https://github.com/soumith/convnet-benchmarks/tree/master/caffe/imagenet_winners (ConvNet benchmarks; files were updated to use newer Caffe prototxt format but are functionally equivalent). Intel C++ compiler ver. 17.0.2 20170213, Intel MKL small libraries version 2018.0.20170425. Caffe run with “numactl -l“.
Platform: 2S Intel® Xeon® CPU E5-2697 v2 2.70GHz (12 cores), HT enabled, turbo enabled, scaling governor set to “performance” via intel_pstate driver, 256GB DDR3-1600 ECC RAM. CentOS Linux release 7.3.1611 (Core), Linux kernel 3.10.0-514.21.1.el7.x86_64. SSD: Intel® SSD 520 Series 240GB, 2.5in SATA 6Gb/s, 25nm, MLC. Performance measured with: Environment variables: KMP_AFFINITY='granularity=fine, compact,1,0‘, OMP_NUM_THREADS=24, CPU Freq set with cpupower frequency-set -d 2.7G -u 3.5G -g performance Deep Learning Frameworks: Caffe*: (http://github.com/intel/caffe/), revision b0ef3236528a2c7d2988f249d347d5fdae831236. Inference measured with “caffe time --forward_only” command, training measured with “caffe time” command. For “ConvNet” topologies, dummy dataset was used. For other topologies, data was stored on local storage and cached in memory before training. Topology specs from https://github.com/intel/caffe/tree/master/models/intel_optimized_models (GoogLeNet, AlexNet, and ResNet-50), https://github.com/intel/caffe/tree/master/models/default_vgg_19 (VGG-19), and https://github.com/soumith/convnet-benchmarks/tree/master/caffe/imagenet_winners (ConvNet benchmarks; files were updated to use newer Caffe prototxt format but are functionally equivalent). GCC 4.8.5, Intel MKL small libraries version 2017.0.2.20170110.
3Up to 65% lower 4-year TCO estimate example based on equivalent rack performance using VMware ESXi* virtualized consolidation workload comparing 20 installed 2-socket servers with Intel® Xeon® processor E5-2690 (formerly “Sandy Bridge-EP”) running VMware ESXi* 6.0 GA using Guest OS RHEL* 6.4 compared at a total cost of $919,362 to 5 new Intel® Xeon® Platinum 8180 (Skylake) running VMware ESXi* 6.0 U3 GA using Guest OS RHEL* 6 64 bit at a total cost of $320,879 including basic acquisition. Server pricing assumptions based on current OEM retail published pricing for 2-socket server with Intel® Xeon® processor E5-2690 v4 and 2 CPUs in 4–socket server using E7-8890 v4 – subject to change based on actual pricing of systems offered.
1Up to 5x claim based on OLTP warehouse workload: 1-Node, 4 x Intel® Xeon® processor E7-4870 on Emerald Ridge with 512 GB total memory on Oracle Linux* 6.4 using Oracle 12c* running 800 warehouses. Benchmark: HammerDB, Score: 2.46322e+006 higher is better vs. 1-Node, 4 x Intel® Xeon® Platinum 8180 processor on Lightning Ridge SKX with 768 GB total memory on Red Hat Enterprise Linux* 7.3 using Oracle* 12.2.0.1 (including database and grid) with 800 warehouses. Score: 1.2423e+007.
2Intel® Xeon® Platinum 8180 processor compared to Intel® Xeon® processor E5-2699 v4 NOTE: 113x gain in last 2 years, using optimized frameworks & optimized Intel® MKL Libraries compared to Intel® Xeon® processor E5-2699 v3 with BVLC-Caffe Platform: 2S Intel® Xeon® Platinum 8180 CPU 2.50GHz (28 cores), HT disabled, turbo disabled, scaling governor set to “performance” via intel_pstate driver, 384GB DDR4-2666 ECC RAM. CentOS* Linux release 7.3.1611 (Core), Linux kernel 3.10.0-514.10.2.el7.x86_64. SSD: Intel® SSD DC S3700 Series (800GB, 2.5in SATA 6Gb/s, 25nm, MLC). Performance measured with: Environment variables: KMP_AFFINITY='granularity=fine, compact‘, OMP_NUM_THREADS=56, CPU Freq set with cpupower frequency-set -d 2.5G -u 3.8G -g performance Deep Learning Frameworks: Caffe*: (http://github.com/intel/caffe/), revision f96b759f71b2281835f690af267158b82b150b5c. Inference measured with “caffe time --forward_only” command, training measured with “caffe time” command. For “ConvNet” topologies, dummy dataset was used. For other topologies, data was stored on local storage and cached in memory before training. Topology specs from https://github.com/intel/caffe/tree/master/models/intel_optimized_models (GoogLeNet, AlexNet, and ResNet-50), https://github.com/intel/caffe/tree/master/models/default_vgg_19 (VGG-19), and https://github.com/soumith/convnet-benchmarks/tree/master/caffe/imagenet_winners (ConvNet benchmarks; files were updated to use newer Caffe prototxt format but are functionally equivalent). Intel C++ compiler ver. 17.0.2 20170213, Intel MKL small libraries version 2018.0.20170425. Caffe run with “numactl -l“.
Platform: 2S Intel® Xeon® CPU E5-2697 v2 2.70GHz (12 cores), HT enabled, turbo enabled, scaling governor set to “performance” via intel_pstate driver, 256GB DDR3-1600 ECC RAM. CentOS Linux release 7.3.1611 (Core), Linux kernel 3.10.0-514.21.1.el7.x86_64. SSD: Intel® SSD 520 Series 240GB, 2.5in SATA 6Gb/s, 25nm, MLC. Performance measured with: Environment variables: KMP_AFFINITY='granularity=fine, compact,1,0‘, OMP_NUM_THREADS=24, CPU Freq set with cpupower frequency-set -d 2.7G -u 3.5G -g performance Deep Learning Frameworks: Caffe*: (http://github.com/intel/caffe/), revision b0ef3236528a2c7d2988f249d347d5fdae831236. Inference measured with “caffe time --forward_only” command, training measured with “caffe time” command. For “ConvNet” topologies, dummy dataset was used. For other topologies, data was stored on local storage and cached in memory before training. Topology specs from https://github.com/intel/caffe/tree/master/models/intel_optimized_models (GoogLeNet, AlexNet, and ResNet-50), https://github.com/intel/caffe/tree/master/models/default_vgg_19 (VGG-19), and https://github.com/soumith/convnet-benchmarks/tree/master/caffe/imagenet_winners (ConvNet benchmarks; files were updated to use newer Caffe prototxt format but are functionally equivalent). GCC 4.8.5, Intel MKL small libraries version 2017.0.2.20170110.
3Up to 65% lower 4-year TCO estimate example based on equivalent rack performance using VMware ESXi* virtualized consolidation workload comparing 20 installed 2-socket servers with Intel® Xeon® processor E5-2690 (formerly “Sandy Bridge-EP”) running VMware ESXi* 6.0 GA using Guest OS RHEL* 6.4 compared at a total cost of $919,362 to 5 new Intel® Xeon® Platinum 8180 (Skylake) running VMware ESXi* 6.0 U3 GA using Guest OS RHEL* 6 64 bit at a total cost of $320,879 including basic acquisition. Server pricing assumptions based on current OEM retail published pricing for 2-socket server with Intel® Xeon® processor E5-2690 v4 and 2 CPUs in 4–socket server using E7-8890 v4 – subject to change based on actual pricing of systems offered.