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To improve ROS 2 performance on NVIDIA Jetson, first measure the real workload, then change one factor at a time. The right power mode, middleware, executor arrangement, and process layout depend on the exact Jetson board and SKU, Jetson Linux or JetPack release, ROS 2 distribution, and workload. There is no single configuration or performance gain established for every Jetson system.
Start with a repeatable baseline
Record the system configuration before tuning. This makes comparisons meaningful and helps distinguish a platform limit from a ROS graph or network issue.
- Jetson board and SKU, Jetson Linux or JetPack release, and selected power mode.
- ROS 2 distribution and RMW implementation.
- Node graph, executor arrangement, QoS settings, and whether components share a process.
- Representative sensor input, message types and sizes, topic rates, and network topology.
- Test duration, cooling setup, and ambient conditions.
Run the application with representative input rather than relying on an idle node or a synthetic publisher. Keep the input, duration, and measurement method consistent when comparing runs. This is a reproducible engineering approach, not an official ROS 2 or NVIDIA benchmark protocol.
Measure ROS behavior and Jetson resources together
Device utilization alone does not identify a bottleneck. Correlate resource trends with the timing and throughput of the ROS messages that matter to the application.
#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.
| What to inspect | Tool or method | What it helps establish |
|---|---|---|
| Subscription message behavior | ROS 2 Topic Statistics, where applicable | Characterizes subscription performance and can help diagnose issues. ROS 2 Kilted documentation describes it as a way to characterize system performance or help diagnose present issues. |
| Processor and memory activity | tegrastats |
Shows memory and processor usage on Jetson. NVIDIA’s Jetson Linux Developer Guide R38.4 describes tegrastats as reporting those usages. |
| CPU/GPU frequencies and power mode | tegrastats; where supported in the installed release, jetson_clocks --show |
Provides context for active device behavior and observed frequency settings. Check the documentation for the target release. |
Capture these observations while the representative workload is running, and record the power mode and thermal conditions alongside ROS latency or throughput measurements. High utilization by itself is not proof that a component is the bottleneck; look for a relationship between resource trends and workload timing.
Check the limits of the specific Jetson platform
Jetson power modes are platform- and SKU-dependent. NVIDIA documents that the selected mode affects CPU core availability and maximum CPU/GPU frequencies, so a mode ID or label from another model is not a safe tuning recipe.
- Query the available modes on the target system with
sudo nvpmodel -q --verbose. NVIDIA’s Jetson Linux Developer Guide R36.5 validation guidance documents this query. - Check the device’s supported mode and frequency behavior in documentation for its exact platform and software release.
- Observe frequency behavior during the workload with
tegrastatsor, where documented for that release,jetson_clocks --show. - Compare workload results across supported settings while recording thermal behavior and power conditions.
NVIDIA’s R39.2 platform power and performance documentation discusses power, thermal, and electrical management as platform features. Selecting a maximum supported power mode sets the platform’s maximum supported power; it does not guarantee a particular sustained application speed or make that setting the most energy-efficient choice. Use measured performance and operating conditions to decide whether a mode is appropriate.
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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.
Find delays in callbacks and execution
If latency or missed timing correlates with particular callbacks, inspect how long callbacks run and whether long-running work can delay time-sensitive callbacks in the executor arrangement.
The ROS 2 Humble rclc_examples documentation illustrates timer events being dropped while one executor handles a long subscription callback. That example demonstrates a possible scheduling issue in that rclc setup; it is not a benchmark of every ROS 2 client library or every rclcpp executor.
- Measure callback duration and compare it with the timing requirements of timers and incoming messages.
- Check whether a long callback coincides with delayed or missed work elsewhere in the graph.
- Evaluate executor changes against the actual callback mix instead of assuming that a particular executor is universally faster.
Evaluate composition using the actual node graph
ROS 2 composition allows components to run in one process. The ROS 2 Jazzy composition documentation describes how to compose components, but does not establish a quantified Jetson speed gain.
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.
Compare the same representative graph before and after composition, measuring latency and resource use. Include deployment constraints and fault isolation in the decision: a shared process changes the process layout, but whether that is a benefit depends on the application and its operational needs.
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Choose middleware and QoS for the deployment
ROS 2 supports multiple RMW implementations. The ROS 2 Kilted middleware documentation identifies platform availability, resource utilization, and computation footprint as relevant selection factors; it does not name a universally fastest implementation for Jetson.
Compare candidates with the deployment’s message sizes and rates, network topology, latency goals, and reliability and durability requirements. Check that the chosen RMW is supported with the target ROS distribution and that the QoS settings match across communicating endpoints. ROS documentation cautions that DDS implementations can communicate in many cases, but cross-vendor compatibility is not guaranteed in all circumstances. Keep systems on a consistent ROS version and RMW where practical, and validate interoperability in the target setup.
Change one variable at a time
- Choose one change, such as a supported power mode, a process layout, or a middleware configuration.
- Run the same workload with the same input and duration as the baseline.
- Compare the same ROS timing and throughput indicators, resource observations, and thermal conditions.
- Record the board, software versions, RMW, QoS, power mode, and test conditions with the result before making another change.
This approach makes it easier to tell which change affected performance. Available documentation describes monitoring tools, supported platform behavior, and ROS mechanisms; it does not provide a controlled universal benchmark comparing Jetson models, middleware choices, composition, or power modes. Treat any claimed gain as workload-specific unless it has been measured under conditions that match the target system.
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