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HPE R6B53C Nvidia A100 40Gb Gpu Module For

Unleash unprecedented performance and flexibility with the HPE R6B53C Nvidia A100 40GB GPU Module. This powerhouse GPU is designed to accelerate a diverse range of workloads, from AI and data analytics to high-performance computing (HPC) tasks. Featuring 40GB of high-bandwidth memory, it delivers unparalleled computational power, ensuring rapid processing and reliability. Ideal for modern data centers, it supports multi-instance GPU technology, enabling efficient resource allocation and optimization. Transform your infrastructure and stay ahead with the HPE Nvidia A100 40GB GPU Module.

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HPE R6B53C Nvidia A100 40Gb Gpu Module For

Features

  • Enhanced Performance: Powered by the Nvidia Ampere architecture, the HPE R6B53C Nvidia A100 40Gb GPU Module is designed for unparalleled computational power.
  • High Memory Capacity: Equipped with 40GB of high-bandwidth memory (HBM2), which is ideal for running complex machine learning models and large datasets.
  • Multi-Instance GPU (MIG) Technology: Allows up to 7 instances per GPU, maximizing resource utilization and running multiple workloads simultaneously.
  • Superior Scalability: Perfect for data center solutions, scaling from a single NVIDIA A100 GPU to multiple nodes.
  • Multi-GPU Support: Compatible with various multi-GPU configurations, enhancing parallel computing capabilities.
  • Energy Efficient: Delivers high performance while maintaining energy efficiency, making it suitable for large-scale deployments.
  • Enhanced Security: Features secure boot and encrypted memory options, ensuring data protection and integrity.

Technical Specifications

  • GPU Architecture: Nvidia Ampere
  • Memory: 40GB HBM2
  • Memory Bandwidth: 1.6 TB/s
  • CUDA Cores: 6912
  • TDP: 400W
  • Tensor Cores: 432
  • MIG Supported Instances: Up to 7
  • Form Factor: SXM4
  • Interface: NVLink
  • HBM2 Bandwidth: 1.6 TB/s
  • Base Clock Speed: 1.41 GHz
  • Boost Clock Speed: Up to 1.71 GHz
  • ECC Memory: Yes

Frequent Ask Questions

  • What is the HPE R6B53C Nvidia A100 40Gb GPU Module?

    The HPE R6B53C Nvidia A100 40Gb GPU Module is a high-performance computing component designed for artificial intelligence, data analytics, and scientific computing applications. It delivers accelerated performance and efficiency in data centers.

  • What are the key features of the Nvidia A100 40Gb GPU Module?

    Key features of the Nvidia A100 40Gb GPU Module include 40 GB of high-bandwidth memory, multi-instance GPU technology, tensor cores for AI workloads, and compatibility with various high-performance computing infrastructures.

  • How does the Nvidia A100 improve AI and machine learning workloads?

    The Nvidia A100 GPU enhances AI and machine learning workloads by providing superior computational power, enabling faster model training and inference times. Its tensor cores are optimized for deep learning tasks, leading to significant improvements in performance.

  • Is the HPE R6B53C Nvidia A100 compatible with all server systems?

    The HPE R6B53C Nvidia A100 GPU Module is designed to be compatible with HPE server systems. Compatibility with other systems may vary and should be confirmed with the manufacturer.

  • What type of applications benefit from using the Nvidia A100 40Gb GPU Module?

    Applications that benefit from the Nvidia A100 40Gb GPU Module include high-performance computing, AI and machine learning, data analytics, deep learning, scientific research, and any other workloads requiring significant computational power.

  • Can the Nvidia A100 GPU Module be used for both training and inference tasks?

    Yes, the Nvidia A100 GPU Module is designed to handle both training and inference tasks efficiently. Its versatile architecture allows it to switch between these workloads seamlessly, delivering optimal performance for both.

  • What is the difference between the Nvidia A100 and previous generation GPUs?

    The Nvidia A100 GPU offers substantial improvements over previous generation GPUs, including higher memory capacity, increased computational power, advanced tensor cores, and multi-instance GPU technology. These enhancements lead to better performance, efficiency, and scalability for a wide range of applications.

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