3U VPX, NVIDIA Turing 11 TFLOPS, WOLF FGX, Coaxial Connections

Kod produktu: VPX3U-RTX5000E-COAX-CV

Cechy produktu:

  • NVIDIA Quadro Turing TU104 GPU, 10.9 TFLOPS peak performance*, 3072 CUDA Cores, 384 Tensor Cores
  • SOSA™ Aligned with support for SOSA slot profile
  • WOLF FGX provides support for additional video formats such as SDI and Analog
  • Module power: configurable from 50 to 150W
  • 3G/HD-SDI and two CVBS/NTSC/PAL outputs
  • Turing GPGPU parallel processing:
    • 3072 CUDA® cores
    • CUDA Toolkit 10, CUDA Compute capability 7.5
    • OpenCL™ 1.2, DirectX® 12, OpenGL 4.6, OpenGL ES 3.2, Vulkan™ 1.0
  • 384 Turing Cores for advanced AI inference
  • 48 Ray Tracing Cores for advanced rendering
  • 16 GB GDDR6 memory provides twice the bandwidth of the previous generation of GDDR5 memory
  • HEVC (H.265) and AVC (H.264) Turing NVENC and NVDEC hardware acceleration with up to 8K encode resolution and B-frame support
  • PCI Express x16
  • Daisy Chain and NTB Options Available
  • Windows and Linux drivers

The VPX3U-RTX5000E-COAX-CV (WOLF-1349) module includes both an NVIDIA® Quadro® Turing™ RTX5000 embedded GPU and a WOLF FGX in a rugged 3U VPX module. The RTX5000 includes CUDA cores for parallel processing, Tensor cores for dedicated AI inference and ray tracing cores for superior rendering speeds. The WOLF FGX provides video conversion to formats which are not native to the GPU, such as SDI and analog formats. The module is SOSA™ Aligned with support for the SOSA payload slot profile.

Getting data into and out of the module is an important consideration for this high-speed GPU. Support for GDDR6 memory provides twice the bandwidth of the previous generation’s GDDR5 memory.

The Turing GPU with its Tensor cores provides this module with the underlying architecture required for an efficient AI inference engine. Intended to work in conjunction with TensorRT, CUDA and CuDNN, the Turing Tensor Core design adds INT8 and INT4 matrix operations, while continuing to support FP16 for higher precision workloads.

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