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You can assemble a Flutter interface for Jetson health and ROS 2 application data from documented components, but no source here establishes a ready-made NVIDIA Flutter dashboard or measures end-to-end latency. Choose either a ROS 2 WebSocket client or an HTTP/metrics layer, then make freshness and connection state visible alongside the values.
How the telemetry path fits together
Think of the dashboard as four layers: producers on or near the Jetson, a transport or aggregation layer, the Flutter client, and operational rules for freshness and update rates. The components below document viable patterns; they do not prove a single integrated deployment or guarantee that every metric is available on every device.
- Produce telemetry. Use ROS 2 diagnostics and application topics for node and robot state. Add Jetson-specific diagnostics for board health. Include DeepStream metrics only if the deployment actually uses a DeepStream video pipeline.
- Choose a transport. Connect Flutter to ROS 2 over the documented WebSocket approach, or have the app consume selected metrics and alert operations through an HTTP/metrics layer. Validate message types, network topology, and security controls for the deployment.
- Present operational state. Organize the interface around connection status, data age, compute and thermal constraints, and whether important topics or streams are active.
- Control collection and updates. Set bounded update rates and measure resource use on the target workload. Higher Prometheus scrape frequency and larger metric sets increase CPU and RAM use, according to NVIDIA’s monitoring documentation.
What can provide telemetry on a Jetson?
Jetson Platform Services metrics and alerts
NVIDIA Jetson Platform Services describes exporters that expose application metrics, a web server for alert operations and HTTP clients, Prometheus for scraping and alert evaluation, Alertmanager for alert routing, and Grafana for visualization. Its documentation says the web server’s HTTP alert operations are also available to other clients, including mobile users. That makes an API-based Flutter client an architectural possibility, not a documented Flutter SDK or a guarantee that every Jetson deployment exposes identical endpoints. NVIDIA Jetson Platform Services monitoring
The documented examples include GPU utilization, clock frequency and temperature; CPU core usage; EMC bandwidth and encoder/decoder frequencies; camera identity, status and failure counts; stream FPS; and general system measures such as RAM, CPU, network, and filesystem usage. Custom exporters can expose application status metrics, so the displayed set depends on what the deployment exports.
#1 Best Overall
- The NVIDIA Jetson Orin Nano Developer Kit sets a new standard for creating entry-level AI-powered robots, smart drones, and intelligent cameras,and simplifies getting started with the Jetson Orin Nano series. Compact design, lots of connectors and up to 40 TOPS of AI performance make this developer kit perfect for transforming your visionary concepts into reality. With up to 80X the performance of Jetson Nano, it can run all modern AI models, including transformer and advanced robotics models.
- The developer kit comprises a Jetson Orin Nano 8GB module and a reference carrier board that can accommodate all Orin Nano and Orin NX modules, providing an ideal platform for prototyping your next-gen edge AI product. The Jetson Orin Nano 8GB module features an Ampere GPU and a 6-core ARM CPU, enabling multiple concurrent AI application pipelines and high-performance inference. The carrier board boasts a wide array of connectors, including two MIPI CSI connectors supporting camera modules with up to 4-lanes, allowing higher resolution and frame rate than before.
- Jetson runs the NVIDIA AI software stack, with available use-case-specific application frameworks, including NVIDIA 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 with NVIDIA TAO Toolkit for fine-tuning pretrained AI models from the NGC catalog.
- 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.
- Jetson Orin modules are unmatched in performance and efficiency for robots and other autonomous machines, and give you the flexibility to create the next generation of AI solutions with the latest NVIDIA technology. Together with the world-standard NVIDIA AI software stack and an ecosystem of services and products, your road to market has never been faster.
ROS 2 diagnostics for board health
The current Isaac ROS Jetson release documentation describes a package that wraps jetson_stats output and publishes diagnostic messages. Listed capabilities include system monitoring, fan control, GPU frequency control, power management, thermal-throttling monitoring, memory usage, and JetPack version detection. The page reports testing with ROS 2 Jazzy on Jetson platforms and says x86_64 is unsupported. Check the release documentation for the specific hardware and software combination before following version-specific installation instructions. Isaac ROS Jetson documentation
NVIDIA’s separate ros2_jetson_stats repository describes ROS 2 diagnostic messages and services for CPU/GPU, memory, power, and temperature data, as well as fan settings, power mode, and Jetson clocks. Its README reports testing with ROS 2 Eloquent, so it is evidence of the wrapper pattern rather than current-distribution compatibility. For present compatibility, use the current Isaac ROS release documentation. NVIDIA-AI-IOT ros2_jetson_stats repository
Rank #2
- 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.
Topic-rate debugging
greenwave_monitor is a ROS 2-side terminal dashboard for topic rates and monitoring. Its repository states compatibility with Humble, Iron, Jazzy, Kilted, and Rolling across Ubuntu 22.04, 24.04, and 26.04. It can help inspect a system during development, but it is not a Flutter interface. NVIDIA-ISAAC-ROS greenwave_monitor repository
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If the application uses DeepStream, NVIDIA documents OpenTelemetry support through a pipeline plugin with OTLP/HTTP export. Documented metrics include stream FPS, pipeline latency, frame number, stream count, CPU and GPU utilization, and RAM use. These are conditional on the DeepStream pipeline; they are not automatic telemetry from every ROS 2 application. NVIDIA also notes that gpu_memory_gb does not apply to aarch64 Jetson Thor: because it uses unified memory, the metric returns -1. DeepStream documentation
Rank #3
- 【Core Parameters】★AI Perf: 117/157 TOPS★GPU: 1024-core N-VI-DIA Ampere architecture GPU with 32 Tensor Cores★CPU: 8-core Arm Cortex-A78AE v8.2 64-bit CPU 2MB L2 + 4MB L3★Memory: 16GB 128-bit LPDDR5 | 102.4GB/s★Storage: Supports external NVMe 【Note: This kit does not include a SSD and pre-installed system. User need to provide your own NVMe M.2 SSD of at least 256GB and flash the operating system onto it yourself. 】
- 【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.
- 【Revolutionize the Industry】Jetson Orin NX modules deliver unmatched performance and efficiency for small, low-power robotics and autonomous machines, making them ideal for drones, handheld devices, and more. The module can be easily used in advanced applications in manufacturing, logistics, retail, agriculture, medical and life sciences, and comes in a highly compact and energy-efficient package.
- 【Revolutionizing AI with Unmatched Performance】The Jetson Orin NX system module adopts the Ampere architecture GPU, a new generation of deep learning and vision accelerators, high-speed I/O, and fast memory bandwidth to support multiple AI application processes. Granular structured sparsity to improve the operating throughput of Tensor Core, and can use larger and more complex AI model development solutions in natural language understanding, 3D perception and multi-sensor fusion.
- 【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.
Choose a client integration path
| Path | Useful data scope | Client approach | Key qualification |
|---|---|---|---|
| Jetson Platform Services | Board and system metrics, application metrics, and alert operations | HTTP or metrics-oriented intermediary | Available endpoints depend on the deployment; documentation does not provide a Flutter SDK. |
| Isaac ROS Jetson diagnostics | Jetson health published as ROS 2 diagnostics | ROS 2 client or intermediary | Current page reports ROS 2 Jazzy testing on Jetson and no x86_64 support; verify the chosen release. |
| DeepStream OpenTelemetry | Video-pipeline metrics such as stream FPS and pipeline latency | OTLP/HTTP export, optionally through an aggregator | Only applies when using the documented DeepStream setup. |
| Flutter ROS 2 widgets | ROS 2 telemetry and example camera, lidar, and teleoperation views | WebSocket ROS connection in Flutter | The package is pre-1.0 and warns that its API may change. |
The ros2_flutter package documentation describes widgets built on ros2_client, sharing a ROS connection through a WebSocket URI. It is a candidate integration path, not an official NVIDIA component or a production-readiness guarantee. Review compatibility and security before adopting it. The documentation does not establish the package’s frame rate or resource cost. ros2_flutter package documentation
Prefer the ROS 2 client path when the Flutter app needs selected ROS messages directly and the deployment supports the required connection. Prefer an HTTP or metrics intermediary when the app needs a curated set of health values or alert operations without taking on a broader ROS client role. Neither path is universally best; choose according to message types, deployment, and security requirements.
Rank #4
- The Nvidia Jetson Xavier Nx Developer Kit Includes A Power-Efficient, Compact Jetson Xavier Nx Module For Ai Edge Devices. It Benefits From New Cloud-Native Support And Accelerates The Nvidia Software Stack In As Little As 10 W With More Than 10X The Performance Of Its Widely Adopted Predecessor, Jetson Tx2. The Capability To Develop And Test Power-Efficient, Small Form-Factor Solutions With Accurate, Multi-Modal Ai Inference Opens The Door For New Breakthrough Products.
- Developers Can Now Take Advantage Of Cloud-Native Support To Transform The Experience Of Developing And Deploying Ai Software To Edge Devices. Pre-Trained Ai Models From Nvidia Ngc, Together With The Nvidia Transfer Learning Toolkit, Provide A Faster Path To Inference With Optimized Ai Networks, While Containerized Deployment To Jetson Devices Allows Flexible And Seamless Updates.
- The Developer Kit Is Supported By The Entire Nvidia Software Stack, Including Accelerated Sdks And The Latest Nvidia Tools For Application Development And Optimization. When Combined With Jetson Xavier Nx, This Powerful Stack Helps You Create Innovative Solutions For Manufacturing, Logistics, Retail, Service, Agriculture, Smart City, Healthcare And Life Sciences, And More.
- Ease Of Development And Speed Of Deployment—Together With A Unique Combination Of Form-Factor, Performance, And Power Advantage—Make Jetson Xavier Nx The Most Flexible And Scalable Platform To Get To Market Fast And Continuously Update Over The Lifetime Of A Product.
Make freshness and failure visible in Flutter
A numeric value without an age indicator can look healthy after its producer or connection has stopped. Treat connectivity and freshness as first-class UI data, not as assumptions.
- Show whether the robot connection is connected or disconnected.
- Display the timestamp or age of the latest value, and distinguish stale data from a current reading.
- Keep a last-known value visually separate from a live value; do not silently present it as current.
- Show topic or stream activity where it matters to the operator, such as whether expected messages are arriving.
- Bound update rates and avoid refreshing every metric as quickly as possible. NVIDIA warns that higher scrape frequency and quantity increase CPU and RAM use.
These are interface and operational recommendations, not experimentally validated guarantees. Set stale thresholds and refresh intervals against the system’s actual telemetry cadence and workload; the cited documentation does not establish a universally safe interval.
Best Value
- GPU:2560-core NVIDIA Blackwell architecture GPU with 96 fifth-gen Tensor Cores
- AI Performance:2070 TFLOPS
What “real-time” means here
The documented components support monitoring and telemetry delivery, but the available material does not establish deterministic timing or an end-to-end Flutter dashboard latency benchmark. Do not treat the dashboard as hard-deadline or safety-critical instrumentation without defining and measuring the required end-to-end timing under the target workload and network conditions.
NVIDIA’s DeepStream documentation gives typical examples of 25–30 FPS depending on source and typical pipeline latency of 30–100 ms, with lower latency preferred. Those are documentation examples for pipeline behavior, not results for this Flutter dashboard and not a Jetson performance guarantee. DeepStream documentation
Quick Recap
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