Additional requirements apply when using advanced features such as analysis, machine learning, deep learning and AI-assisted tools. These functions place higher demands on system memory, CPU capabilities and system configuration than basic visualization. If the corresponding requirements are not met, arivis Pro may fall back to CPU processing, which typically results in lower performance. Certain features may also not be available.

Category

Requirement

Notes

Analysis
  • 32 GB RAM (minimum)
  • 16 GB RAM per CPU core (recommended)
  • Advanced analysis operations use tiling and multithreading and therefore require more memory.
  • More memory is required to fully utilize all available CPU cores.

Machine Learning/
Deep Learning
(GPU acceleration)
  • Ensure that the GPU Support Package is selected during installation.

Deep Learning Trainer
  • NVIDIA GPU with latest driver
  • CUDA compute capabilities (version 7.5 or higher)
    see https://developer.nvidia.com/cuda-gpus whether your GPU supports it.
  • GPU that provides at least 8GB of memory (highly recommended)
  • Python 3 environment (optional installer component)

AI model store Deep Learning Segmentation
(Instance segmentation)

– Docker Desktop
(Tested successfully with version 4.77.0)
  • Windows 11 64-bit
    • Enterprise, Pro or Education version 23H2 (build 22631) or higher.
  • BIOS-level Hardware Virtualization support must be enabled
  • 64 bit processor with Second Level Address Translation (SLAT)
  • WSL 2 feature on Windows has to be enabled, Version 2.1.5 or later (see https://docs.docker.com/desktop/wsl/)
    • If your version of Windows does not support the WSL 2 feature: Hyper-V and Containers Windows Features must be enabled.
  • NVIDIA GPU with latest drivers supporting WSL 2 GPU Paravirtualization.
    • GPU support is only available on Windows with the WSL2 backend.
    • CUDA Compute Capability ≥ 7.0 (Volta or newer)
    • At least 8 GB CPU RAM

– Alternative setup
(If Docker Desktop or WSL2 cannot be used locally, you can use a second machine to run Docker loads remotely.)
  • Separate Linux machine (a real PC or a VM) e.g. Debian or Ubuntu
  • NVIDIA GPU (8GB RAM or higher) with CUDA Compute Capability ≥ 7.0 (Volta or newer)
  • Docker engine, configured for Docker Remote Access and NVIDIA Container Toolkit

AI-assisted mode of the Draw Tool
  • NVIDIA GPU (8GB or more recommended)
  • GPU Support Package (optional) is installed
  • Support for AI-assisted Draw Tool (optional) with additional larger AI models is highly recommended
  • Note: If these requirements are not met, calculations will default to the CPU, resulting in slower performance and reduced resolution. A smaller AI model will be used, which may decrease accuracy, especially if the GPU has less than 8GB of memory or the optional larger models are not installed.