Hewlett Packard Enterprise R4B02C

HPE Alveo U50 FPGA Accelerator Card

What's New

• Flexible to fit your needs and available in two options: Alveo™ U50 and Alveo™ U250.
• Xilinx® Alveo™ U50 delivers compute, networking, and storage acceleration in an efficient 75W, small form factor, and is armed with 100 GbE networking, PCIe Gen4, and HBM2.
• Xilinx® Alveo U250 offers 1.3M LUTs, 11.5K DSP slices, 64 GB of DDR4 memory, dual 100 Gbps network interfaces, and delivers increased performance over CPUs on key workloads at a fraction of the cost.

Key Features

Adaptable and Accessible Compute Acceleration

Xilinx® Accelerators for HPE are designed for high-bandwidth, low-latency servers, networking, and storage applications to bring higher value into hybrid cloud deployments.

Advancements in process technology and device architecture have allowed Xilinx® FPGAs to be tuned for customer applications.

Flexible Agility and Powerful Options

For more control, the optimized throughput with low latency and power efficiency from compute, networking, or storage workloads span applications including big data analytics, deep learning inference, high performance computing, financial services, networking, and computational storage.

As demands on data center infrastructure increases and workloads evolve, acceleration, flexibility, and performance has never been more critical. Fixed function hardware accelerators and CPU acceleration cannot keep up to these rapidly changing demands.

Innovation Integration

Xilinx® Alveo™ accelerator cards for HPE ProLiant servers bring together powerful and adaptable FPGA compute fabric, high speed memory, and high speed networking and interfaces to effectively solve some of the more challenging compute, networking, and storage acceleration workloads.

Xilinx® Alveo™ accelerated solutions with HPE products, software and services span a broad range of applications and workloads including Big Data analytics, deep learning inference, high-performance computing, financial services, networking, and computational storage.