# Training PC Requirements

The following requirements describe the suggested system for the ViDi application training and development PC.

- USB 2.0

  USB port for a permanent Cognex Security Dongle (containing the training license) connection via USB 2.0.

  Note:

  The VisionPro, Designer and ViDi software require that a valid Cognex Security Dongle be installed directly to PCs running the software during all phases of operation (programming, processing, training, testing, etc.). Any attempts to temporarily remove, substitute, or share a Cognex Security Dongle may cause your system to operate incorrectly, and may result in the loss of data.

  When ViDi is configured for the Client/Server functionality, and a computer has been configured as a server, the Cognex Security Dongle must be attached to the server, but the client(s) does not need a Cognex Security Dongle.

- CPU

  When selecting a CPU, a higher CPU clock speed rate and multiple core processors will result in higher runtime tool execution. If your application will be relying on the Blue Locate tool, it is more sensitive to clock speed rates, particularly with complex model matching applications.

- System Memory (RAM)

  When selecting the system RAM, specify the greater of:

  - The sum of all GPU memory. For example, if you have four NVIDIA GeForce® RTX™ 2080 Ti GPUs, which each have 11 GB of memory, the PC should have 44 GB of RAM.
  - The RAM should be one and a half times the typical workspace size. For example, if your typical workspace is 20 GB, the PC should have a minimum of 30 GB of RAM.

- System Storage

  Cognex recommends the use of a solid-state drive (SSD), with a minimum amount of free space that is two times the typical workspace size, times the number of concurrent users.

- GPU

  When selecting a GPU, Cognex only supports NVIDIA GPUs. Consider the following when choosing an NVIDIA GPU:

  At a minimum, an NVIDIA® CUDA® enabled GPU. When selecting an NVIDIA GPU, a unit with a higher core clock frequency, CUDA cores and Tensor cores will result in faster computational speeds. Due to this, Cognex strongly recommends the use of NVIDIA Quadro and Tesla GPUs for the following reasons:

  - These GPUs support the compute-optimized Tesla Compute Cluster (TCC) mode.
  - These GPUs are designed and tested for continuous duty-cycle operation.
  - These GPUs undergo rigorous testing and qualification by NVIDIA.