HPE P05470-001 

NVIDIA V100 32GB Computational Accelerator

The HPE P05470-001 NVIDIA V100 32GB Computational Accelerator is a high-performance GPU designed to deliver massive parallel processing power for artificial intelligence (AI), machine learning, deep learning, and high-performance computing (HPC) environments. Built on NVIDIA Volta architecture, this Accelerator features 32GB of high-bandwidth memory (HBM2) and 640 Tensor Cores, enabling efficient processing of large models, complex algorithms, and advanced simulations. It is ideal for data scientists, researchers, and developers seeking accelerated training and inference performance. Designed to integrate seamlessly with HPE server infrastructure, the V100 ensures reliable, scalable, and energy-efficient GPU acceleration for the most demanding data center workloads.

Features

With its groundbreaking Volta architecture, the NVIDIA V100 GPU delivers up to 125 teraFLOPS of deep learning performance, significantly reducing time-to-insight and training durations. The HPE P05470-001 leverages this power to enhance parallel processing, enabling real-time model training and inference across enterprise AI and scientific computing environments. This card features 32GB of HBM2 memory with 900 GB/s memory bandwidth, ensuring seamless performance even when managing massive datasets or performing compute-intensive simulations. Designed to support mixed-precision workloads, the V100 accelerates FP16, FP32, and FP64 operations, making it ideal for hybrid computing environments. When deployed in HPE ProLiant or Apollo servers, it delivers optimal thermal performance, simplified deployment, and compatibility with enterprise-grade tools.

Advanced Options

The HPE HPEP05470-001 NVIDIA V100 is engineered for consistent and efficient performance in enterprise and scientific workloads. Its passive-cooled design integrates directly into HPE data center platforms, offering optimal thermal control and energy efficiency. The 32GB of HBM2 memory provides the speed and capacity necessary to process massive data sets in AI and scientific computing, while Tensor Core technology accelerates deep learning operations such as matrix multiplication and convolution.