The NVIDIA Quadro GP100, powered by NVIDIA’s Pascal GPU architecture, is equipped with the most advanced visualization and simulation capabilities to meet the needs of the most demanding professional workflows. A single GP100 with 3580 FP32/1792 FP64 CUDA Cores has incredible horsepower to render photorealistic design concepts interactively, create extremely detailed 3D models, and run intensive CAE simulations to validate designs and evaluate design prototypes in immersive VR. You can even connect two GP100’s with NVIDIA NVLink technology to scale performance with an amazing 32GB of combined HBM2 ultra-high bandwidth memory.
Features
- The NVIDIA Quadro GP100 can run FEA, CFD, CEM, and other GPU-accelerated analysis codes faster. With 16GB of HBM memory, larger simulations can fit into GPU memory. Users can connect two GP100 cards with NVLink to provide 32GB of HBM memory to run the largest simulations.
- NVIDIA’s Pascal GPU technology enables the GP100 to run compute and graphics tasks simultaneously, allowing users to run simulations and create 3D models at the same time.
- NVIDIA Quadro GP100 supports multiple displays at up to 5K resolution.
- Use real-world physics, lighting, and materials during interactive design and visualize with photo-realistic image quality.
- Integrate simulation during design with finer time steps, more depth of field, larger assemblies, and more detail in components.
- NVIDIA Iray plug-ins enable designers to visualize designs with photorealistic image quality using physically based lights and materials as an integrated part of their workflow.
- Combine two GP100 cards with NVLink to provide incredible rendering power for the largest, most demanding rendering workloads.
- Create immersive VR environments with larger, more complex designs that can be experienced at scale, complete with virtual audio and touch integration.
- The GP100 GPU support for deep learning software enables designers to take advantage of next-generation generative design technology, creating optimal designs rapidly through machine learning.
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