Name: 876911-001 HP NVIDIA Tesla V100 16GB HBM2 4096-Bit CUDA 5120 PCI Express 3.0 x16 Video Graphics Card
Brand: HPE
SKU: 876911-001
Price: 249.00 USD
Availability: InStock
Rating: 4.8 (119 reviews)

Product Overview
The HP NVIDIA Tesla V100 offers high-performance GPU acceleration primarily used in scientific research and AI development environments. It fits into servers and workstations where intensive parallel processing and large memory capacity are essential to handle complex workloads effectively.

General Information
Brand	HPE
Part Number	876911-001

Technical Information
Chipset	Nvidia
Bus Interface	PCI Express 3.0 x16
Supported APIs	OpenCL, CUDA
Output Interface	None

Memory
Memory Size	16GB
Interface	HBM2
Memory Bus	4096-bit

Physical Characteristics
Slot Width	Dual-slot
Weight	3.00
Condition	Refurbished

Miscellaneous
Assembly Required	Yes
Eco Friendly	Yes
Compliance Standards	WEEE, RoHS, CE, FCC

Product Description
The HP NVIDIA Tesla V100 is a powerful GPU designed for machine learning, scientific simulations, and data analytics. Often found in high-performance computing setups and data centers, this card handles demanding processing tasks efficiently. Built for researchers, engineers, and data scientists, it accelerates complex computations and deep learning training. Its high memory bandwidth and numerous CUDA cores allow it to process large datasets and models smoothly. Key Features Equipped with 16GB of HBM2 memory supporting high throughput 5120 CUDA cores to speed up parallel processing tasks Uses PCI Express 3.0 x16 for broad compatibility and fast communication Optimized for compute-intensive applications like AI and simulations Supports advanced GPU architectures for better efficiency This card is typically deployed in data centers and research labs where heavy computation is routine. It plays a crucial role in shortening processing times and enabling more complex analyses.

Use Cases
The HP NVIDIA Tesla V100 is typically utilized in high-performance computing environments requiring substantial parallel processing capabilities and large memory bandwidth. It is ideal for research institutions, AI development labs, and enterprise data centers where complex simulations, machine learning training, and data analytics are routine tasks. How It's Used: Accelerating deep learning model training in AI research labs to reduce time-to-insight. Enhancing computational fluid dynamics simulations within engineering R&D departments. Running large-scale data analysis workloads in enterprise data centers for real-time decision making. Supporting virtualization in GPU-accelerated cloud platforms for scalable resource allocation. Facilitating scientific computations in national laboratories requiring high memory throughput. Driving rendering and visualization tasks in media production environments with intensive GPU demands. By leveraging the Tesla V100’s architecture, organizations can achieve significant improvements in processing efficiency and workload scalability. The card’s design aligns with the demands of evolving computational tasks, enabling smoother integration into existing infrastructure and workflows.