System requirements for VisionaryAI Suite

VisionaryAI Suite is built for advanced AI analysis of images, video, audio, and metadata. It uses modern hardware to deliver fast, capable analysis on your own machine. Exact limits can depend on the models and build you run—see also the download page and your product documentation.

Minimum

  • 🖥 Operating system: Windows 10 / 11 (64-bit)
  • ⚙ Processor: Intel Core i5 / AMD Ryzen 5 or better
  • 🧠 RAM: 16 GB
  • 🎮 Graphics: NVIDIA GPU with CUDA support
  • 📊 VRAM: at least 6 GB
  • 💾 Storage: SSD recommended
  • 🌐 Internet: required for installation and updates

Recommended

  • 🖥 Operating system: Windows 11 (64-bit)
  • ⚙ Processor: Intel Core i7 / i9 or AMD Ryzen 7 / 9
  • 🧠 RAM: 32–64 GB
  • 🎮 Graphics: NVIDIA RTX series
  • 📊 VRAM: 12–24 GB for best AI performance
  • 💾 Storage: NVMe SSD
  • 🚀 AI workflow: tuned for local GPU-accelerated analysis

Performance & VRAM — what it roughly means

VRAM is the GPU’s onboard memory; it determines how large the models and internal frame sizes can be before the run slows down or falls back to less aggressive settings. The figures below are ballpark guides for local analysis—actual speed depends on the model pack, video sampling rate (keyframes), file size, and whether several AI stages run back-to-back.

6 GB VRAM (minimum)

Entry-level workable: cards such as RTX-class 6 GB parts meet our stated minimum.

  • Images: most single-image jobs at ordinary resolution run acceptably; very large masters may need downscaling or one heavy task at a time.
  • Video: expect longer queue times—shorter clips and fewer analysed frames per minute help; prefer one intensive AI pipeline at once.

8 GB VRAM

Noticeably more headroom for vision models day to day.

  • Images: more comfortable at higher working resolutions or when several steps chain in one job.
  • Video: 1080p-class keyframes and multi-file batches are often practical; demanding presets may still need tuning.

12 GB VRAM

A strong “sweet spot” for mixed stills-and-video libraries.

  • Images: extra margin for heavier models run in sequence.
  • Video: longer takes and denser sampling usually feel smoother with fewer stalls between clips.

Beyond 12 GB VRAM (e.g. 16–24 GB)

Workstation / power-user territory—cuts wait time when archives are large or settings are aggressive.

  • Images & video: higher internal working size, shorter batch times, and more parallel slack where the app and driver allow.
  • GPU generation still matters: newer RTX architectures tend to deliver more throughput per GB than older cards.
  • System RAM: video paths often consume more host memory for decode and buffering than a single still—16 GB is our minimum; 32 GB+ pays off for big batches and multitasking.
  • CPU: affects how fast frames can be extracted and can bottleneck some preprocessing—a modern 6–8-core CPU pairs well with a capable GPU.
  • Storage: a fast SSD reduces thumb-twiddling when thousands of files are opened in succession, especially with video.

These examples are illustrative, not guarantees of seconds-per-image or real-time multiples. Keep GPU drivers current and follow the guidance for your specific VisionaryAI Suite build.

Built for demanding AI workflows

VisionaryAI Suite is aimed at people who work with large media libraries, AI-driven metadata extraction, transcription, object detection, and automated structuring of big datasets. A powerful PC delivers dramatically faster analysis and a much smoother day-to-day experience.