Generate C/C++/CUDA Code from an AI Model

Generate deployable C/C++ or CUDA code from an AI model using MATLAB Coder or GPU Coder. The workflow follows a common pattern regardless of model framework: load, inspect, write entry-point, generate MEX, verify, then generate production code.

When to Use

• User wants to generate C/C++/CUDA code from an AI model (PyTorch, LiteRT)
• User has a model file (.pt2, .tflite) and wants to load it into MATLAB
• User wants MEX acceleration for an AI model
• User wants to generate CUDA code or GPU-accelerated MEX from an AI model
• User wants to deploy an AI model to hardware
• User wants to use a PyTorch or LiteRT model in Simulink (simulation or code generation)
• User wants to verify AI model numerics between the source framework and MATLAB

When NOT to Use

• General MATLAB Coder usage (codegen syntax, config tuning, writing codegen-ready code)
• Editable dlnetwork for Deep Learning Toolbox workflows (quantization, compression, transfer learning), use importNetworkFromPyTorch (PyTorch), importNetworkFromTensorFlow (SavedModel), or importNetworkFromKeras (.keras/.h5) which return a dlnetwork. For deployment of an editable dlnetwork with model compression (INT8 quantization via dlquantizer, pruning, projection) or exportNetworkToSimulink workflows, use matlab-deploy-embedded-ai (Pattern 1).
• Training or fine-tuning, this skill is for inference code generation only

Supported Frameworks

Framework	Model format	Load function	Status
PyTorch	.pt2	loadPyTorchExportedProgram	Supported (R2026a+)
LiteRT / TFLite	.tflite	loadLiteRTModel	Supported (R2026a+)

For PyTorch-specific details (API routing, entry-point pattern, export workflow, data layout, common mistakes): see references/pytorch-workflow.md.