VPX6-4955

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The VPX6-4955 delivers incredible processing power from not one, but two NVIDIA Quadro Turing TU104 GPUs, providing a remarkable 6144 CUDA cores for parallel processing and a 50% improvement in performance per CUDA core compared to the previous Pascal generation. What’s more, the Turing architecture’s Tensor Cores accelerate the tensor/matrix computation used for deep learning neural network training and inference operations, answering the growing demand for artificial intelligence and high-performance processing in deployed EW and ISR applications.

Bring new levels of performance to your ISR and EW applications with the VPX6-4955:

  • Cutting-edge Turing GPU architecture - The NVIDIA Turing architecture provides a 50% improvement in performance per CUDA core compared to the previous Pascal generation. Its redesigned memory path combines shared memory, texture caching, and memory load caching into one unit, resulting in twice the bandwidth and over twice the capacity for L1 cache and common workloads than the previous Pascal generation.
  • Support for artificial intelligence and machine learning - The VPX6-4955’s dual TU104 GPUs provide a total of 768 Tensor Cores, designed to speed up the tensor/matrix computation used for deep learning neural network training, for dedicated AI inference.
  • Lightning-speed data processing - Moving data quickly and efficiently is paramount for high-speed GPUs. The VPX6-4955 uses GDDR6 memory, which provides twice the bandwidth of the GDDR5 memory used in previous generations. Incorporating a PCIe switch, this module is configurable for compatibility with various OpenVPX slot profiles.

Key Features

  • Dual NVIDIA Quadro Turing TU104 (RTX5000E) GPUs for 22 TFLOPS & 22 TIPS
  • 6144 CUDA® cores, 768 Tensor Cores, 96 RT Cores
  • 32 GB GDDR6 256-bit memory
  • Max memory bandwidth each: 448 GB/s
  • PCIe Gen 3 x16 switch
  • 8 independent Display Port++  video outputs

 

Applications

  • ISR and EW applications requiring the highest performing GPGPU processing
  • SWaP-constrained deep learning inference that can benefit from the largest number of Tensor Cores in 6U OpenVPX
  • High-performance radar, SIGINT, EO/IR, sensor fusion, processing and display, and autonomous vehicles
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