Minimum & Recommended Hardware Requirements

🍱 Visionbox

• Windows, Mac, or Linux
• Docker 1.2+
• 2 GB of RAM
• visionbox-gpu requires a Nvidia GPU (with compute capability 5.2+) & Nvidia Driver
• CPU that supports AVX instruction set

• 2GB of RAM or better
• Intel I5 Core or better
• Nvidia GTX GPU or better
• Docker 19.03 +

💻 SDK

• Linux, Mac, or Windows (Windows SDK only supports C++ API and has other limitations)
• 1 GB Ram (2 GB if using FULL or TFV5 face recognition models).
• CPU that supports AVX instruction set
• 1 to N identification - 1250 Bytes RAM / template
• For GPU Support:
• The x86-64 GPU enabled SDK supports NVIDIA GPUs with GPU Compute Capability 5.2+, and currently supports CUDA 10.1 and CUDA 11.2. Both are only compatible with Linux.
• Determine your GPU compute capability here

• Linux
• 8 GB of Ram or more
• Intel i5 CPU or better (4 cores or more) with support for AVX2
• Nvidia GPU (GTX or better)

⚙️ Embedded SDK

• ARMv7-A
• For GPU Support:
• The AArch64 GPU enabled SDK supports NVIDIA GPUs with GPU Compute Capability 5.3+, and currently supports CUDA 10.2 (default on NVIDIA Jetson devices). Requires Ubuntu 18.04.

• ARMv8-A
• AArch64 (64 bit execution state)
• Nvidia Jetson Xavier NX

Deprecated

🖥️ Trueface Aware

• 16 GB of RAM
• i5 Intel CPU or Better
• 512 GB SSD
• Nvidia GPU with Comptheute Capability 6.0+ (determine your GPU compute capability here)
• Up to 4 1080-resolution cameras

• 32 GB of RAM
• i7 Intel CPU or Better
• NVMe SSD
• 512 GB of storage
• Up to 6 1080-resolution cameras

🐍 Python SDK

• Windows, Mac, or Linux
• Python 2.7 +
• 1 GB of RAM
• GPU version requires NVIDIA GPU
• GPU version requires CUDA 8.0 or higher

• Windows, Mac, or Linux
• 2 GB of RAM
• Python 3.6 +
• CUDA 9.2 +
• Nvidia driver 440.33 or higher
• GPU Version requires mxnet-gpu: pip install mxnet-cu100