Who is this for?
Hybrid Compute is built for developers working on GPU-accelerated image processing across platforms.
Target Developers
macOS / iOS Developers
Need CUDA-like GPU compute on Apple Silicon via the Metal compatibility shim.
GPU / Graphics Engineers
Working with CUDA, Metal, or building cross-platform GPU compute solutions.
Image Processing Engineers
Computer vision, photo/video applications, medical imaging pipelines.
ML / AI Engineers
Need GPU-accelerated image preprocessing for machine learning workflows.
Cross-platform Developers
Building applications that run on macOS, Linux, and Windows.
DevOps Engineers
Hybrid cloud/local GPU processing workflows and CI/CD pipelines.
Game Developers
GPU texture processing, visual effects, real-time image manipulation.
Scientific Computing
Research image analysis, satellite imagery, microscopy data processing.
Use Cases
Photo Editing Apps
Real-time filters, upscaling, and color adjustments with GPU acceleration.
Medical Imaging
Process large DICOM images, CT scans, and X-rays with local privacy.
Video Processing
Frame-by-frame processing, batch upscaling, and format conversion.
Satellite Imagery
Large geospatial image processing with hybrid cloud/local workflows.
E-commerce
Product image optimization, watermarking, and batch resizing.
Game Development
Texture pipeline tools, asset processing, and real-time effects.
Why Developers Choose Hybrid Compute
Cross-platform: Write CUDA once, run on macOS via Metal, Linux, and Windows.
Hybrid workflow: Split processing between local CPU and cloud GPU.
No lock-in: Open source, BSD licensed, use stb_image or OpenCV.
REST API: Simple HTTP endpoints for integration into any stack.