The NVIDIA 900-82888-0040-000 is a high-performance graphics card designed for demanding applications that require exceptional processing power and graphical fidelity. This model, part of NVIDIA’s professional line of GPUs, is engineered to deliver outstanding performance for tasks such as 3D rendering, video editing, scientific computing, and machine learning.

Key Features

  • GPU Architecture: Based on NVIDIA’s cutting-edge architecture, providing superior performance and energy efficiency.
  • CUDA Cores: A substantial number of CUDA cores to handle parallel processing tasks efficiently.
  • Memory Configuration: Equipped with a significant amount of high-speed GDDR memory, ensuring smooth performance for memory-intensive applications.
  • Cooling Solution: Advanced cooling mechanisms to maintain optimal operating temperatures, enhancing stability and longevity.
  • Form Factor: Designed to fit in standard workstation and server environments, ensuring compatibility with a wide range of systems.

Detailed Specifications

  • GPU Architecture: Pascal
  • CUDA Cores: 3584
  • Base Clock: 1480 MHz
  • Boost Clock: 1582 MHz
  • Memory Configuration: 16GB GDDR5
  • Memory Interface: 256-bit
  • Memory Bandwidth: Up to 288.4 GB/s
  • Power Consumption: 250 Watts
  • Connectors: Multiple DisplayPort 1.4 outputs, HDMI, and DVI-D.
  • Form Factor: Dual-slot, full-height
  • Cooling: Active cooling with dual fans
  • Dimensions: 10.5″ length

Performance and Applications

  • 3D Rendering: Exceptional performance in rendering complex 3D models and scenes, making it ideal for artists and designers.
  • Video Editing: Accelerates video processing tasks, including 4K and 8K video editing, thanks to its powerful CUDA cores and large memory bandwidth.
  • Scientific Computing: Suitable for simulations and data analysis in scientific research due to its high parallel processing capabilities.
  • Machine Learning: Provides the computational power needed for training and deploying machine learning models, enhancing productivity and reducing training times.

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