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MXM GPU MODULE NVIDIA RTX4090-16GB/130W
0,00 €

RTX4090/16GB GDDR6 MXM GPU Module for Edge AI computing , 82 x 105 mm with 130W low-power consumption to provide single-precision 38.127 TFLOPS and integer 686 TOPS computing capability.

  • AI Chip : RTX4090 Ada Lovelace
  • Memory : 16GB GDDR6 256bit
  • Computing: 38.127 TFLOPS/686 TOPS/9728 CUDA/304Tensor/76 RT Core
  • Dimension : MXM 3.1 TYPE B : 82 x 105 mm
  • The best choice for embedded edge AI application
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MXM GPU MODULE NVIDIA RTX4080-12GB/80W
0,00 €

NVIDIA RTX4080/12GB GDDR6 MXM GPU Module for Edge AI computing , 82 x 105 mm with 80W low-power consumption to provide single-precision 30 TFLOPS and integer 542 TOPS computing capability.

  • AI Chip : RTX4080 Ada Lovelace
  • Memory : 12GB GDDR6 192 bit
  • Computing: 30 TFLOPS/542 TOPS/7424 CUDA/232Tensor/80 RT Core
  • Dimension : MXM 3.1 TYPE A : 82 x 70 mm
  • The best choice for embedded edge AI application

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MXM GPU MODULE NVIDIA RTX4080-12GB/125W
0,00 €

NVIDIA RTX4080/12GB GDDR6 MXM GPU Module for Edge AI computing , 82 x 105 mm with 125W low-power consumption to provide single-precision 30 TFLOPS and integer 542 TOPS computing capability.

  • AI Chip : RTX4080 Ada Lovelace
  • Memory : 12GB GDDR6 192 bit
  • Computing: 30 TFLOPS/542 TOPS/7424 CUDA/232Tensor/80 RT Core
  • Dimension : MXM 3.1 TYPE A : 82 x 70 mm
  • The best choice for embedded edge AI application
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MXM GPU MODULE NVIDIA RTX4070-8GB/65W
0,00 €

NVIDIA RTX4070/8GB GDDR6 MXM GPU Module for Edge AI computing , 82mm x 70mm with 65W low-power consumption to provide single-precision 15.71 TFLOPS and integer 321 TOPS computing capability.

  • AI Chip : RTX4070 Ada Lovelace
  • Memory : 8GB GDDR6 128bit
  • Computing: 15.71 TFLOPS/321 TOPS/4608 CUDA/144 Tensor/36 RT Core
  • Dimension : MXM 3.1 TYPE A : 82 x 70 mm
  • 65W power consumption, no external PSU required: draw power directly from the MXM slot
  • The best choice for embedded edge AI application
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MXM GPU MODULE NVIDIA RTX4060-8GB/60W
0,00 €

NVIDIA RTX4060/8GB GDDR6 MXM GPU Module for Edge AI computing, 82mm x 70mm with 60W low-power consumption to provide single-precision 12.22 TFLOPS and integer 232 TOPS computing capability.

  • AI Chip : RTX4060 Ada Lovelace
  • Memory : 8GB GDDR6 128bit
  • Computing: 12.22 TFLOPS/232 TOPS/3072 CUDA/96 Tensor/24 RT Core
  • Dimension : MXM 3.1 TYPE A : 82 x 70 mm
  • 60W power consumption, no external PSU required: draw power directly from the MXM slot
  • The best choice for embedded edge AI application
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MXM GPU MODULE NVIDIA RTX4050-6GB/65W
0,00 €

NVIDIA RTX4050/6GB GDDR6  MXM GPU Module for Edge AI computing , 82mm x 70mm with 65W low-power consumption to provide single-precision 11.835 TFLOPS and integer 194 TOPS computing capability.

  • AI Chip : RTX4050 Ada Lovelace
  • Memory : 6GB GDDR6 96bit
  • Computing: 11.835 TFLOPS/194 TOPS/2560 CUDA/80 Tensor/20 RT Core
  • Dimension : MXM 3.1 TYPE A : 82 x 70 mm
  • 65W power consumption, no external PSU required: draw power directly from the MXM slot
  • The best choice for embedded edge AI application
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MXM GPU MODULE NVIDIA RTX4050-6GB/35W
0,00 €

MXM GPU Module for Edge AI computing , 82mm x 70mm , NVIDIA RTX4050/6GB GDDR6  with 35W low-power consumption to provide single-precision 8.218 TFLOPS and integer 194 TOPS computing capability.

  • AI Chip : RTX4050 Ada Lovelace
  • Memory : 6GB GDDR6 96bit
  • Computing: 8.218TFLOPS/194 TOPS/2560 CUDA/80 Tensor/20 RT Core
  • Dimension : MXM 3.1 TYPE A : 82 x 70 mm
  • 35W power consumption, no external PSU required: draw power directly from the MXM slot
  • The best choice for embedded edge AI application

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MXM GPU MODULE NVIDIA MX550-4GB/35W
0,00 €

NVIDIA MX550/4GB GDDR6 MXM GPU module which is the best inference choice for embedded edge AI application , 82mm x 70mm with 35W low-power consumption to provide single-precision 4.5 TFLOPS and 1024 CUDA computing capability.

  • AI Chip : MX550 ,  Turing
  • Memory : 4GB  GDDR6 64 bit
  • Computing: 4.5 TFLOPS/1024 CUDA/32 Tensor/16 RT Core
  • Dimension : MXM 3.1 TYPE A : 82 x 70 mm
  • 35W power consumption, no external PSU required: draw power directly from the MXM slot
  • The best inference choice for embedded edge AI application

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