2nd Generation Intel® Xeon® Scalable Processors, formerly Cascade Lake, with Intel® C620 Series Chipsets (Purley refresh), features built-in Intel® Deep Learning Boost and delivers high-performance inference and vision for AI workloads. It consolidates diverse IoT workloads, handles massive datasets and enables near-real-time transactions. Now you can get even better built-in deep learning capabilities, speed deployment, and lower total cost of ownership (TCO) with CPU-optimized software toolkits and frameworks such as Intel® Distribution of OpenVINO™ Toolkit.

Key Features

Intel® Deep Learning Boost

Accelerates AI/deep learning/vision workloads up to 14X 1 the inference throughput performance over previous generation processors.

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Intel® Optane™ DC persistent memory

Speed workloads and time to insight with this new revolutionary memory product for affordable, persistent, and large memory.

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Integrated Intel® QuickAssist Technology (Intel® QAT)

Data compression and cryptography acceleration, frees the host processor, and enhances data transport and protection across server, storage, network, and VM migration. Integrated in the chipset.

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Intel® Resource Director Technology for Determinism

Extend Quality of Service (QoS) with memory bandwidth allocation.

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Enhanced Security

Hardware mitigations for side-channel exploits help protect systems and data by hardening the platform against any malicious attacks.

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Extended Availability of Support

15-year product availability and 10-year use-case reliability helps protect your investment.

Highest Performance

Processors

SKUs optimized for highest per-core performance

Processor Cores Base Non-AVX
Speed (GHz)
TDP (W) IoT
Options
Available
Ordering Code
Intel® Xeon® Platinum 8280 Processor 28 2.7 205 - CD8069504228001
Intel® Xeon® Platinum 8270 Processor 26 2.7 205 - CD8069504195201
Intel® Xeon® Platinum 8268 Processor 24 2.9 205 - CD8069504195101
Intel® Xeon® Platinum 8256 Processor 4 3.8 105 - CD8069504194701
Intel® Xeon® Gold 6254 Processor
18 3.1 200 - CD8069504194501
Intel® Xeon® Gold 6246 Processor 12 3.3 165 - -
Intel® Xeon® Gold 6244 Processor 8 3.6 150 - CD8069504194202
Intel® Xeon® Gold 6242 Processor
16 2.8 150 - CD8069504194101
Intel® Xeon® Gold 6234 Processor 8 3.4 130 - -
Intel® Xeon® Gold 6226 Processor 12 2.7 125 Yes -
Intel® Xeon® Gold 5222 Processor 4 3.8 105 - CD8069504193501
Intel® Xeon® Gold 5217 Processor 8 3 115 - CD8069504214302
Intel® Xeon® Gold 5215 Processor 10 2.5 85 Yes CD8069504214002
Intel® Xeon® Silver 4215 Processor
8 2.5 85 Yes CD8069504212701

Balanced Energy Efficiency

Processors

SKUs optimized for balanced energy efficient performance per watt

Processor Cores Base Non-AVX
Speed (GHz)
TDP (W) IoT
Options
Available
Ordering Code
Intel® Xeon® Platinum 8276 Processor 28 2.2 165 - CD8069504195501
Intel® Xeon® Platinum 8260 Processor 24 2.4 165 - CD8069504201101
Intel® Xeon® Platinum 8253 Processor 16 2.2 125 - CD8069504194601
Intel® Xeon® Gold 6252 Processor 24 2.1 150 - CD8069504194401
Intel® Xeon® Gold 6248 Processor
20 2.5 150 - CD8069504194301
Intel® Xeon® Gold 6240 Processor 18 2.6 150 - CD8069504194001
Intel® Xeon® Gold 6238 Processor
22 2.1 140 - -
Intel® Xeon® Gold 6230 Processor
20 2.1 125 Yes CD8069504193701
Intel® Xeon® Gold 5220 Processor 18 2.2 125 - CD8069504214601
Intel® Xeon® Gold 5218 Processor 16 2.3 125 - CD8069504193301
Intel® Xeon® Silver 4216 Processor 16 2.1 100 Yes CD8069504213901
Intel® Xeon® Silver 4214 Processor 12 2.2 85 Yes CD8069504212601
Intel® Xeon® Silver 4210 Processor
10 2.2 85 Yes CD8069503956302
Intel® Xeon® Silver 4208 Processor 8 2.1 85 - CD8069503956401
Intel® Xeon® Bronze 3204 Processor 6 1.9 85 - CD8069503956700

Extended Reliability & Memory

Processors

SKUs optimized for extended reliability and memory

Processor Cores Base Non-AVX
Speed (GHz)
TDP (W) IoT
Options
Available
Ordering Code
Intel® Xeon® Platinum 8280L Processor 28 2.7 205 - CD8069504228201
Intel® Xeon® Platinum 8280M Processor 28 2.7 205
- CD8069504228101
Intel® Xeon® Platinum 8276L Processor 28 2.2 165 - CD8069504195301
Intel® Xeon® Platinum 8276M Processor 28 2.2 165 - CD8069504195401
Intel® Xeon® Platinum 8260L Processor 24 2.4 165 - CD8069504201001
Intel® Xeon® Platinum 8260M Processor 24 2.4 165 - CD8069504201201
Intel® Xeon® Gold 6240L Processor 18 2.6 150 - -
Intel® Xeon® Gold 6240M Processor 18 2.6 150 - -
Intel® Xeon® Gold 6238L Processor 22 2.1 140 - -
Intel® Xeon® Gold 6238M Processor 22 2.1 140 - -
Intel® Xeon® Gold 6238T Processor
22 1.9 125 Yes CD8069504200401
Intel® Xeon® Gold 6230T Processor
20 2.1 125 - -
Intel® Xeon® Gold 5220T Processor
18 1.9 105 - -
Intel® Xeon® Gold 5218T Processor
16 2.1 105 Yes CD8069503955702
Intel® Xeon® Gold 5215L Processor 10 2.5 85 - CD8069504214202
Intel® Xeon® Gold 5215M Processor 10 2.5 85 - CD8069504214102
Intel® Xeon® Silver 4209T Processor
8 2.2 70 Yes CD8069503956900

Specialized

Processors

SKUs specialized for specific workloads and supporting Intel® Speed Select

Processor Cores Base Non-AVX
Speed (GHz)
TDP (W) IoT
Options
Available
Ordering Code
Intel® Xeon® Platinum 8260Y Processor 24 2.4 165 - CD8069504200902
Intel® Xeon® Gold 6262V Processor
24 1.9 135 - -
Intel® Xeon® Gold 6252N Processor
24 2.3 150 - -
Intel® Xeon® Gold 6240Y Processor 16 2.6 150 - CD8069504200501
Intel® Xeon® Gold 6230N Processor
20 2.3 125 - -
Intel® Xeon® Gold 6222V Processor
20 1.8 115 - -
Intel® Xeon® Gold 5220S Processor
18 2.7 125 - -
Intel® Xeon® Gold 5218N Processor 16 2.3 105 - CD8069504289900
Intel® Xeon® Silver 4214Y Processor 12 2.2 85 - CD8069504294401

Chipsets

Chipset 10Gb/1Gb
Ethernet
Ports
TDP (W) PCIe* Uplink Intel® QuickAssist
Technology
IoT
Options
Available
Ordering Code
Intel® C629 Chipset 4/4 28.6 X16 Yes - EY82C629
Intel® C628 Chipset 4/4 26.3 x16 Yes - EY82C628
Intel® C627 Chipset 4/4 28.6 x16 Yes - EY82C627
Intel® C626 Chipset 4/4 23 x16 Yes - EY82C626
Intel® C625 Chipset 4/4 21 x16 Yes - EY82C625
Intel® C624 Chipset 4/4 19 x16 - Yes EY82C624
Intel® C622 Chipset 2/4 17 x8 - Yes EY82C622
Intel® C621 Chipset 0/4 15 x1 - Yes EY82C621

Supported Software

OS Type Operating System 2 (Targeted for Support) Support 3 Distribution BIOS
Linux Red Hat* Enterprise Linux 7.5 Red Hat

American Megatrends Inc

Insyde Software

Phoenix Technologies

BYOSOFT

SUSE* Linux Enterprise Server 12 SP4, 15 SUSE, Open Source SUSE
Ubuntu* 18.04 LTS Canonical, Open Source Canonical
Yocto* Linux v4.19.8  Intel, Open Source Yocto Project*
FreeBSD 11.2 Open Source Community  
Fedora* Open Source Community
CentOS* Open Source Community
Windows*

Microsoft Windows* Server 2016

Microsoft Windows* Server 2019 LTS

Microsoft Windows* Server RS3, RS4, RS5 (Core/Nano)

Intel, Microsoft Microsoft
VMM Linux KVM Open Source Community  
VMware ESXi* 6.0 u3, 6.5 VMware*, Open Source
Microsoft Windows* Hyper-V Microsoft
Xen* 4.10, 4.11 Open Source Community

Software Tools

Intel® System Studio

Boost performance, power efficiency, and reliability for system and IoT device applications with this all-in-one development tool suite (Windows*, Linux, Android*, VxWorks*, QNX Neutrino RTOS*).

Community Forum

Free Download

Intel® Distribution of OpenVINO™ Toolkit

Make your vision a reality on Intel® platforms—from smart cameras and video surveillance to robotics, transportation, and more (Windows*, Linux, CentOS*).

Community Forum

Free Download

Intel® Data Analytics Acceleration Library

Boost big data analytics and machine learning performance with this easy-to-use library. (Windows*, Linux, macOS*).

Community Forum

Free Download

Intel® Distribution of Python*

Supercharge Python* applications and speed up core computational packages with this performance-oriented distribution (Windows*, Linux, macOS*).

Community Forum

Free Download

Embedded & IoT Optimized Applications

Smart Cities

Whether densely populated or remote, AI applications with Intel® Deep Learning Boost support faster, more accurate security and surveillance even in crowded, complex urban environments

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Retail

Solutions to inform and streamline operations, personalize shopping, and capture data, such as store traffic patterns, to better serve their customers

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Healthcare

Object detection and segmentation identify and compare relevant patterns and other imaging data faster and more accurately, which speeds and improves diagnoses, delivering better outcomes for more patients, and reduced costs for hospitals

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Industrial & Manufacturing

Intel® Deep Learning Boost brings the performance and capabilities that accelerate industrial IoT and manufacturing to advance AI, increase performance, use machine vision for defect detection and quality inspection, and consolidate workloads

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Design Resources

Design-In Tools Store
Accelerate your design process with tools that support our latest platforms. All tools are available for purchase, plus Intel offers a limited selection of embedded development tools for loan at no cost to developers who meet the loan program’s criteria.

Free Design Review Services
Speed your design cycle with Intel’s free-of-charge schematic and layout reviews.

Free Platform Testing Services
Optimize system performance and product design with our comprehensive testing services.

Product and Performance Information

1

1x inference throughput improvement on Intel® Xeon® Platinum 8180 processor (July 2017) baseline: Tested by Intel as of July 11th 2017: 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. 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, synthetic 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 (ResNet-50), 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® Math Kernel Library (Intel® MKL) small libraries version 2018.0.20170425. Caffe run with “numactl -l“.

14x inference throughput improvement on Intel® Xeon® Platinum 8280 processor with Intel® Deep Learning Boost (Intel® DL Boost):
Tested by Intel as of 2/20/2019. 2 socket Intel® Xeon® Platinum 8280 processor, 28 cores HT On Turbo ON Total Memory 384 GB (12 slots/ 32GB/ 2933 MHz), BIOS: SE5C620.86B.0D.01.0271.120720180605 (ucode: 0x200004d), Ubuntu 18.04.1 LTS, kernel 4.15.0-45-generic, SSD 1x sda INTEL SSDSC2BA80 SSD 745.2GB, nvme1n1 INTEL SSDPE2KX040T7 SSD 3.7TB, Deep Learning Framework: Intel® Optimization for Caffe* version: 1.1.3 (commit hash: 7010334f159da247db3fe3a9d96a3116ca06b09a), ICC version 18.0.1, MKL DNN version: v0.17 (commit hash: 830a10059a018cd2634d94195140cf2d8790a75a, model https://github.com/intel/caffe/blob/master/models/intel_optimized_models/int8/resnet50_int8_full_conv.prototxt, BS=64, syntheticData, 4 instance/2 socket, Datatype: INT8 vs. Tested by Intel as of July 11, 2017: 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. Caffe: (https://github.com/intel/caffe/), revision f96b759f71b2281835f690af267158b82b150b5c. Inference measured with “caffe time --forward_only” command, training measured with “caffe time” command. For “ConvNet” topologies, synthetic 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/resnext_50, Intel® C++ Compiler ver. 17.0.2 20170213, Intel® MKL small libraries version 2018.0.20170425. Caffe run with “numactl -l“.

2

This is the OS list that is tested internally and does NOT reflect the OS vendor support for these exact release versions. Please contact respective OS vendor(s) for the release version numbers and support. Several software patches will be upstreamed and will be picked up over time. These will be required to enhance platform support.

3

Intel only provides support for our tools, patches and utilities on the OS. Actual OS support should come from the OS Vendor.