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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →You can use AI in a Flutter app in two different ways: run a compatible model on the user’s device, or make Flutter a client for inference running on an NVIDIA Jetson. These are separate architectures. Flutter’s inference packages do not automatically support every NVIDIA model, and the official documentation reviewed here does not describe a turnkey Flutter-to-Jetson bridge.
Choose where inference will run
Start by deciding which device should process each request. That choice determines the runtime, model format, connectivity, and operational work—not simply whether the interface is written in Flutter.
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| Consideration | Inference inside the Flutter app | Inference on Jetson |
|---|---|---|
| Execution location | The user’s phone, computer, or browser, subject to runtime support. | A Jetson device that hosts a separately implemented inference service. |
| Compatibility | Target operating system, Flutter runtime, supported engine, model artifact, and feature support must align. | The model and inference stack must work on the chosen Jetson configuration; the client and service need an agreed interface. |
| Resources | Fit the model to the user device’s memory and compute resources. | Fit the model and expected concurrent workload to the selected Jetson hardware and power configuration. |
| Connectivity and data | Inference can happen on-device; consider model downloads and any other network needs separately. | Requests require a network path to the service, and the application must account for what data crosses it. |
| Operational work | Distribute and update model files and manage support across device platforms. | Deploy and monitor the service, secure access, manage disconnections and long-running requests, and keep client and server compatible. |
These are architecture trade-offs, not benchmark results. The right choice depends on the actual model, target devices, connectivity, privacy needs, and deployment constraints.
Run a compatible model inside Flutter
Flutter itself is the app framework, not an inference engine. Flutter Edge AI documents opt-in runtime packages for on-device inference across Android, iOS, web, and desktop, with runtime and model support varying by platform. Its documentation discusses LiteRT-LM, MediaPipe, ONNX, and operating-system-provided model paths. Check the current getting started guide, installation instructions, and runtime capabilities before selecting an approach.
#1 Best Overall
- The NVIDIA Jetson AGX Orin 64GB Developer Kit makes it easy to get started with Jetson Orin. Compact size, lots of connectors, and up to 275 TOPS of AI performance make this developer kit perfect for prototyping advanced AI-powered robots and other autonomous machines.
- The developer kit includes a Jetson AGX Orin 64GB module, and can emulate all the Jetson Orin modules. It supports multiple concurrent AI application pipelines with the NVIDIA Ampere GPU architecture, next-generation deep learning and vision accelerators, high-speed IO and fast memory bandwidth. Now you can develop solutions using your largest and most complex AI models to solve problems such as natural language understanding, 3D perception, and multi-sensor fusion.
- Jetson runs the NVIDIA AI software stack, and use-case specific application frameworks are available, including Isaac for robotics, DeepStream for vision AI, and Riva for conversational AI. You can save significant time with NVIDIA Omniverse Replicator for synthetic data generation (SDG), and by using NVIDIA TAO toolkit to fine-tune pretrained AI models from the NGC catalog.
- Jetson ecosystem partners offer additional AI and system software, developer tools, and custom software development. They can also help with cameras and other sensors, as well as carrier boards and design services for your product.
- With the computing capability of more than 8 Jetson AGX Xavier systems in a developer kit that integrates the latest NVIDIA GPU technology with the world’s most advanced deep learning software stack, you’ll have the flexibility to create tomorrow’s AI solution as well as today’s.
Check the model and engine together
Do not assume a model is usable just because its filename or family sounds familiar. Flutter Edge AI’s model documentation explains that the declared file type selects the engine; confirm that the artifact, engine, platform, and desired capability are supported together.
Check the capability you need
Text generation, embeddings, and speech support differ by runtime. A runtime that supports one does not necessarily support the others. Also account for model size, device memory, storage, and whether a relevant operating-system model is available on the intended device.
Plan model delivery and updates
Local inference avoids a request to a separate inference host, but the model still has to be available on the device. Decide how users obtain model files, how updates are delivered, and how the app handles unsupported devices or insufficient resources.
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Rank #2
- AGX Orin 64GB Development Kit makes it easy to get started with AGX Orin. Its compact size, rich interfaces, and AI performance of up to 275 TOPS make it ideal for building advanced AI robots and other autonomous machine prototypes.
- The development kit includes AGX Orin 64GB module and can emulate all Orin modules. It utilizes the Ampere GPU architecture, next-generation deep learning and vision accelerators, high-speed I/O, and fast memory bandwidth. You can leverage the largest and most complex AI models to develop solutions for problems such as natural language understanding, 3D perception, and multi-sensor fusion.
- Jetson runs AI software and provides application frameworks for specific use cases, such as Isaac for robotics, DeepStream for visual AI, and Riva for conversational AI. Using Omniverse Replicator for Synthetic Data Generation (SDG) can save you significant time; while fine-tuning pre-trained AI models from the NGC catalog using the TAO toolkit can further enhance your results.
- Yahboom offers four kits for users to choose from. The AIlarge model voice module utilizes examples of AI large models and multimodal models; it provides 1TB/2TB SSDs with pre-flashed driver image files; and an 8MP USB industrial camera for image processing.
- It offers various online and offline mainstream AI large model development materials. The system is pre-configured with AI vision examples, ROS case studies, and AI large models. It supports offline/online deployment of large models for voice interaction, real-time video analysis, and visual positioning, helping you quickly get started with localized AI agent development.
Use Flutter as a client for Jetson-hosted inference
NVIDIA describes Jetson as an edge-compute platform. Its developer kits are intended for developing and testing software on Jetson-based products, while JetPack is the software suite for developing and deploying Jetson edge applications. See NVIDIA’s Jetson developer kit information.
In this design, Flutter provides the user interface and a separately implemented service on Jetson performs inference. The service boundary is part of your project: the reviewed official sources do not specify a Flutter package, API, transport, or control protocol that connects the two automatically.
Define and secure the service contract
Choose how the Flutter client sends inputs and receives results, define request and response formats, and secure access appropriate to the deployment. The exact API and transport are project decisions; validate them for the actual network, users, and data involved.
Rank #3
- 【Core Parameters】★AI Perf:34-67 TOPS ★GPU:512-core NVIDIA Ampere architecture GPU with 16 Tensor Cores ★CPU:6-core Arm Corte-A78AE v8.2 64-bit CPU 1.5MB L2 + 4MB L3 ★Memory:4GB 64-bit LPDDR5 51 GB/s ★Storage: external NVMe via M.2 Key M (NOTE:SUB Board No SD Card Slot)
- 【Empowered by Large Al Model, Enhanced Human-Computer Interaction】Jetson Orin Super leverages three AI models and incorporates an AI voice interaction module. This multimodal visual system matches the scene being described, enabling environmental awareness and AI visual gameplay. Combined with a large-scale voice module and camera, it enables speech-to-text, semantic analysis, natural conversation, and real-time video analysis, enabling advanced embodied AI applications.
- 【AI Upgrade】Jetson Orin Nano series modules are compact in size but can deliver up to 34-67 TOPS of AI performance, with power consumption ranging from 7 watts to 25 watts. Compared to the Jetson Nano B01, it offers up to 80 times the performance and sets a new standard for entry-level edge AI.
- 【Highly compatible carrier board】Yahboom's carrier board is fully compatible with orin nano module. Compared to carrier boards that use Jetson Nano on the market, the newly upgraded circuit supports 25W power mode, which enables larger and more complex neural networks and fully leverages the performance of the core module. The resources, size, and interfaces of the Yahboom carrier board are consistent with the official board, with the only difference addition of power switch button.
- 【Tutorial materials provided】The JETSON system based on Ubuntu 22.04 provides a complete desktop Linux environment with accelerated graphics, supporting NVIDI-ACUDA 12.6, TensorRT 10.7.0, cuDNN 9.6.0, OpenCV 4.10.0, etc. The performance on AI LLM, VLM and visual Transformer is significantly improved compared with the previous generation.
Handle latency and failures in the interface
Inference requests may take time, and a client can lose its connection to the host. Design the UI to represent pending work, errors, timeouts, and recovery rather than assuming every request returns immediately. Test those behaviors on the network and hardware where the application will run.
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Test on the intended Jetson configuration
Measure the selected model and workload on the actual device, including the expected request volume and concurrency. NVIDIA advertises Jetson Orin Nano series modules with up to 67 TOPS of AI performance and configurable power options from 7 W to 25 W on its Jetson modules page. Those are vendor specifications, not a throughput result for a particular model or Flutter application.
Verify current JetPack details before deployment
JetPack is a versioned software suite, so check NVIDIA’s JetPack downloads and release notes for the release that matches the target device and deployment. At the time of the cited release snapshot, the page listed JetPack 7.2.1 with Jetson Linux 39.2.1 and CUDA 13.2.2; versions can change, so verify the page before choosing a stack.
Keep Flutter AI tools distinct from inference runtimes
Flutter’s AI Toolkit documentation covers Flutter AI tooling. Do not treat the existence of an AI-related Flutter tool as proof that a specific NVIDIA model runs locally or that Flutter can directly control Jetson hardware. Confirm the documented role and compatibility of each component you use.
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