HPE P18065-B21

NVIDIA 32GB Computational Accelerator

The HPE HPE P18065-B21 NVIDIA 32GB Computational Accelerator is purpose-built for data-intensive environments that demand high parallel computing performance. Equipped with 32GB of high-bandwidth memory and powered by NVIDIA cutting-edge architecture, this Accelerator is ideal for AI inferencing, deep learning, scientific simulations, and high-performance computing (HPC) workloads. Designed to seamlessly integrate with HPE ProLiant and Apollo systems, it enables researchers, data scientists, and enterprise users to tackle the most complex computational tasks efficiently. Whether you're building a next-generation AI model or processing massive datasets, this Accelerator delivers the compute density and power efficiency modern data centers require.

Features

Designed for compute-heavy applications, the HPE P18065-B21 leverages NVIDIA parallel processing capabilities to accelerate a wide array of modern workloads. Its 32GB of ultra-fast memory provides the capacity needed for training deep neural networks, running real-time simulations, or delivering GPU-accelerated virtualization. The architecture behind this Accelerator delivers exceptional throughput and improved energy efficiency, making it an ideal fit for large-scale data center deployments.Compatible with HPE server infrastructures, this Accelerator supports industry-standard APIs and frameworks such as CUDA®, TensorRT™, and cuDNN, ensuring smooth integration into existing AI pipelines or HPC clusters. Whether used for edge inference, rendering, or large-scale analytics, the P18065-B21 offers unmatched flexibility and performance in a compact PCIe form factor.

Advanced Options

The HPE P18065-B21 Computational Accelerator is engineered for long-term reliability and performance stability in mission-critical environments. Featuring robust thermal management and power optimization, it operates consistently under sustained workloads. Its efficient architecture enables greater parallel task execution, reducing job completion time for compute-intensive processes such as AI training or seismic analysis.