New Product Introduction

Xilinx ALVEO™ - Adaptable Accelerator Cards

Breathe new life into your data center and on-premises with Xilinx Alveo accelerator cards for Artificial Intelligence, Machine Learning or Data Center Workloads.

Xilinx Alveo product image

Xilinx® Alveo™ Data Center accelerator cards are designed to meet the constantly changing needs of the modern Data Center, providing up to 90X performance increase over CPUs for common workloads, including machine learning inference, video transcoding, and database search and analytics. 

Built on Xilinx 16nm UltraScale™ architecture, Alveo U200 and U250 accelerator cards provide reconfigurable acceleration that can adapt to continual algorithm optimizations, supporting any workload type while reducing overall cost of ownership. 

 

Key benefits

Fast
Highest Performance
Adaptable
Accelerate Any Workload
Accessible
Cloud ↔ On-Premises Mobility
  • Up to 90X higher performance than CPUs on key workloads at one-third the cost
  • Over 4X higher inference throughput3 and 3X latency advantage over GPU-based solutions
  • Machine learning inference to video processing to any workload using the same accelerator card
  • As workload algorithms evolve, use reconfigurable hardware to adapt faster than fixed-function accelerator card product cycles
  • Deploy solutions in the cloud or on-premises interchangeably, scalable to application requirements
  • Applications available for common workloads, or build your own with the Application Developer Tool
 

Accelerator cards that fit your performance needs

Alveo U200 Alveo U250
  • 33.3 Peak INT8 TOPs
  • 77GB/s DDR Memory Bandwidth
  • 38TB/s Internal SRAM Bandwidth
  • 1,341,000 LUTs

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Acceleration applications

Alveo Data Center accelerator cards can deliver dramatic acceleration across a broad set of applications and are reconfigurable to provide an ideal fit for the changing workloads of the modern data center. Compare how Alveo Data Center accelerator cards perform compared to traditional CPU architectures.
 


 

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