# Matlab Deploy AI Model

Generate C/C++ or CUDA code from an AI model (PyTorch, LiteRT) using MATLAB Coder or...

- Repository: https://github.com/matlab/matlab-agentic-toolkit (1237 stars)
- Category: DevOps
- License: see repository
- Last updated: 2026-10-27
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-deploy-ai-model -g -a claude-code -y`
Codex: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-deploy-ai-model -g -a codex -y`
Cursor: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-deploy-ai-model -g -a cursor -y`
Gemini CLI: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-deploy-ai-model -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add matlab/matlab-agentic-toolkit`

## Description

Generate C/C++ or CUDA code from an AI model (PyTorch, LiteRT) using MATLAB Coder or GPU Coder. Use when the user wants to integrate an AI model into an application with code generation as the end goal, generating MEX, CUDA MEX, static library, dynamic library, or executable, or using the model in Simulink for simulation and code generation. Covers PyTorch ExportedProgram (.pt2) via loadPyTorchExportedProgram and LiteRT (.tflite) via loadLiteRTModel (R2026a+). Keywords: PyTorch, torch, .pt2, ExportedProgram, loadPyTorchExportedProgram, invoke, codegen, MEX, CUDA, GPU, C, C++, deploy, AI model, deep learning model, LiteRT, TFLite, TensorFlow Lite, Simulink, slbuild, PyTorch ExportedProgram block, MATLAB Function block, dlosslib, loadLiteRTModel.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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`.

The rest: https://github.com/matlab/matlab-agentic-toolkit/blob/HEAD/skills-catalog/code-generation/matlab-deploy-ai-model/SKILL.md