# NVIDIA Jetson

> Source: https://aiwiki.ai/wiki/nvidia_jetson
> Updated: 2026-07-14
> Categories: AI Hardware, NVIDIA
> License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
> From AI Wiki (https://aiwiki.ai), the free encyclopedia of artificial intelligence. Reuse freely with attribution to "AI Wiki (aiwiki.ai)".

**NVIDIA Jetson** is NVIDIA's family of compact, power-efficient system-on-modules (SoMs) that bring GPU-accelerated computing to machines that cannot lean on a data-center connection: robots, drones, cameras, and other devices that have to sense and decide on their own power budget. First released in 2014, the Jetson line has gone through six architectural generations, and by the mid-2020s its highest-performance modules had become the standard onboard computer for [humanoid robot](/wiki/humanoid_robot) prototyping. Jetson AGX Orin, launched in 2022 with up to 275 trillion operations per second (TOPS) of AI performance, was the module most humanoid-robot teams reached for first; its 2025 successor, [Jetson Thor](/wiki/jetson_thor), pushed further with a Blackwell-generation GPU built specifically for running large models on a robot's own body rather than over a wireless link.[1][2][3]

Jetson modules pair an [NVIDIA](/wiki/nvidia) [GPU](/wiki/gpu) with an Arm CPU and a shared pool of memory on a single circuit board, running a cut-down version of the same CUDA and TensorRT software NVIDIA sells for data-center AI. That software compatibility, as much as any single spec, is why Jetson dominates [robotics](/wiki/robotics) and [edge AI](/wiki/edge_ai) compute: a model trained on NVIDIA's cloud GPUs can usually be optimized and redeployed onto a Jetson module with the same toolchain, instead of being rewritten for different silicon.[4]

## Architecture: a module, not a full computer

A Jetson product is not a complete computer on its own. Each module is a small board, roughly credit-card to deck-of-cards sized depending on the tier, carrying a system-on-chip (SoC) that integrates an Arm CPU, an NVIDIA GPU, memory controllers, and, on most generations, dedicated accelerators for video and vision. Manufacturers plug that module into a carrier board, either their own design or the reference carrier board NVIDIA ships in its developer kits, which supplies power regulation, connectors, and I/O. The SoM approach lets a robotics company move from one compute generation to the next, for example from Orin to Thor, without redesigning its entire electronics stack, as long as the new module fits the same connector standard.[4]

Unlike a discrete graphics card, a Jetson module has no separate video memory: the CPU and GPU share one pool of LPDDR memory, which simplifies moving data between perception and control code but also puts a hard ceiling on how large a model the board can hold. That ceiling became a headline spec in its own right once robotics teams started trying to run large vision-language-action models on the robot itself rather than in the cloud, which is part of why Jetson Thor's memory jumped to 128GB.[5]

NVIDIA sells Jetson in three tiers, in ascending order of performance and price: Nano (entry-level), NX (mid-range), and AGX (the flagship tier used in the top module of each generation). Each hardware generation carries a code name rather than a version number: Tegra K1, Tegra X1, Tegra X2, Xavier, Orin, and Thor, the same six architecture families NVIDIA has shipped, in order, since 2014.[6]

## History and generations

Jetson began in 2014 with the Jetson TK1, a development board built around the Tegra K1 chip and aimed at researchers experimenting with [computer vision](/wiki/computer_vision) and early [deep learning](/wiki/deep_learning) on a mobile power budget. NVIDIA followed with the TX1 (2015) and TX2 (2017), compact credit-card-sized modules that became popular in university robotics labs and early drones because they were among the smallest boards that could run a real convolutional neural network in real time.[6][7]

The 2018 Jetson AGX Xavier marked a shift from research board to industrial product. Its 512-core Volta GPU, with dedicated Tensor Cores and two Deep Learning Accelerator (DLA) blocks, delivered 32 TOPS of INT8 inference, aimed at autonomous machines rather than lab benchmarks.[8] A smaller, cheaper Xavier NX followed in 2019 and shipped in volume in 2020, delivering 21 TOPS on a board small enough to fit inside a delivery robot or a drone.[9] NVIDIA also opened Jetson to hobbyists and students with the Jetson Nano developer kit in March 2019, a $99 board built around a stripped-down Maxwell GPU with no Tensor Cores that nonetheless put a real, if modest, AI accelerator within reach of a maker budget.[10]

| Module | Year | AI performance (peak) | Power (TDP) | Memory |
|---|---|---|---|---|
| Jetson TK1 | 2014 | ~0.3 TFLOPS (FP32, dense) | 10 W | 2 GB LPDDR3 |
| Jetson TX1 | 2015 | ~1.0 TFLOPS (FP16, dense) | 10 W | 4 GB LPDDR4 |
| Jetson TX2 | 2017 | ~1.3 TFLOPS (FP16, dense) | 7.5-15 W | 8 GB LPDDR4 |
| Jetson Nano | 2019 | 0.47 TFLOPS (FP16, dense) | 5-10 W | 4 GB LPDDR4 |
| Jetson AGX Xavier | 2018 | 32 TOPS (INT8, dense) | 10-30 W (up to ~40 W MAX-N) | 32-64 GB LPDDR4x |
| Jetson Xavier NX | 2019/2020 | 21 TOPS (INT8, dense) | 10-20 W | 8 GB LPDDR4x |
| Jetson Orin Nano (Super) | 2023 (2024 update) | up to 67 TOPS (INT8, sparse) | 7-25 W | 4-8 GB LPDDR5 |
| Jetson Orin NX | 2023 | up to 157 TOPS (INT8, sparse) | 10-40 W | 8-16 GB LPDDR5 |
| Jetson AGX Orin | 2022 | up to 275 TOPS (INT8, sparse) | 15-60 W | 32-64 GB LPDDR5 |
| Jetson AGX Thor (T4000) | 2025 | up to 1,200 TFLOPS (FP4, sparse) | 40-70 W | 64 GB LPDDR5X |
| Jetson AGX Thor (T5000) | 2025 | up to 2,070 TFLOPS (FP4, sparse) | 40-130 W | 128 GB LPDDR5X |

Figures are NVIDIA's own published maximums. Xavier and Orin-generation TOPS are measured as INT8 operations, Thor's headline number is FP4 floating-point operations, and pre-Xavier modules predate NVIDIA's TOPS branding and are shown in dense TFLOPS instead; none of these units convert cleanly into one another.[1][3][4][6]

Jetson AGX Orin, announced at NVIDIA's GTC conference with developer kits shipping from March 22, 2022, reset expectations again. Built on NVIDIA's Ampere GPU architecture, the top 64GB configuration delivers up to 275 sparse INT8 TOPS with power configurable between 15 and 60 watts, which NVIDIA described at launch as more than 8 times the processing power of AGX Xavier in a comparable footprint.[11] Smaller Orin NX (up to 157 TOPS, 10-40W) and Orin Nano (originally up to 40 TOPS, 7-15W) modules followed through 2023, filling out the mid-range and entry tiers with the same Ampere architecture and software stack.[1][12] NVIDIA later unlocked extra performance on the entry-level board through a free firmware update: the December 2024 "Orin Nano Super" configuration raised its ceiling from 40 to 67 TOPS and cut the developer kit price from $499 to $249 without changing the underlying silicon.[13]

By the early-to-mid 2020s, independent robotics press described Jetson AGX Orin as the effective standard for humanoid and mobile-robot compute, the module most new entrants reached for by default rather than the exception.[2][14]

## Jetson Thor: the humanoid-era tier

Jetson Thor is NVIDIA's sixth Jetson generation and the first designed explicitly around [physical AI](/wiki/physical_ai) and humanoid robots rather than adapted from an automotive or industrial part. It reached general availability on August 25, 2025. The flagship T5000 module pairs a Blackwell-generation GPU (2,560 CUDA cores and 96 fifth-generation Tensor Cores) with a 14-core Arm Neoverse-V3AE CPU and 128GB of LPDDR5X memory, rated at up to 2,070 FP4 teraflops of AI compute within a 40 to 130 watt envelope; a smaller T4000 variant trims that to 1,200 FP4 teraflops and 64GB of memory in a 40 to 70 watt envelope.[3][5] NVIDIA describes the flagship module as delivering roughly 7.5 times the AI compute and 3.5 times the energy efficiency of AGX Orin at a comparable power draw, with the larger memory pool aimed at letting a 70-billion-parameter-class model run directly on the robot.[5] Developer kits list at $3,499, with production T5000 modules priced from roughly $2,999 at volume, according to NVIDIA and press coverage of the launch.[15]

One architectural change is easy to miss: Thor drops the dedicated Deep Learning Accelerator (DLA) block that shipped on every Jetson from Xavier through Orin, relying instead on the Blackwell GPU's transformer-tuned Tensor Cores to carry the attention-heavy math of modern foundation models.[16][5] That is also why Thor's headline figure is quoted in FP4 teraflops rather than the INT8 TOPS used for Orin and earlier chips; the two numbers describe different arithmetic and are not directly comparable.

Thor's early adopters, as named by NVIDIA and reported in trade press, include [Boston Dynamics](/wiki/boston_dynamics) (Atlas), Agility Robotics (Digit), [Amazon Robotics](/wiki/amazon_robotics), Caterpillar, [Figure](/wiki/figure_ai), Hexagon, and Medtronic's surgical robots.[3][17] Boston Dynamics has said its electric Atlas runs on Jetson Thor and is an early adopter of NVIDIA's [Isaac GR00T](/wiki/isaac_gr00t) foundation models, with its engineers training locomotion and manipulation policies in [Isaac Lab](/wiki/isaac_lab) before deploying them to the robot.[18] [1X Technologies](/wiki/1x_technologies) has said Jetson Thor is, in its own assessment, the only commercially available module that meets the onboard compute requirements of its [1X Neo](/wiki/1x_neo) home robot, which uses it to run large models locally for perception, reasoning, and control rather than depending on a network connection.[19] This article covers Thor's place in the broader Jetson lineup; see the dedicated Jetson Thor article for a full specification breakdown.

## Software stack

Jetson's advantage has never been the silicon alone. NVIDIA pairs every generation with a matching software stack, and that stack is arguably what keeps robotics teams from switching to cheaper hardware.

**JetPack** is the base SDK: a Linux distribution (Jetson Linux, formerly branded L4T for "Linux for Tegra") bundled with CUDA, cuDNN, and TensorRT, plus board-support packages and power-management tools.[20] JetPack 6 covers the Orin generation, while JetPack 7, rolling out through 2026, extends support to Thor and adds a production-grade Yocto Project build alongside the developer-oriented Ubuntu image, a real-time kernel option, and Blackwell's Multi-Instance GPU (MIG) partitioning for running several isolated workloads on one chip.[21][20]

**TensorRT** is NVIDIA's inference optimizer: it takes a trained model exported from PyTorch, TensorFlow, or the ONNX exchange format and compiles it into a runtime engine tuned for a specific Jetson module, using layer fusion and reduced-precision quantization (FP16, INT8, or FP4 on Thor) to cut latency before the model ever runs on the robot.[22] **Isaac ROS** layers robotics-specific, GPU-accelerated packages on top of the open-source ROS 2 framework, covering perception tasks such as stereo depth estimation, object detection, and visual odometry, along with a motion-planning library called Isaac ROS cuMotion.[23] For fixed-camera and infrastructure applications rather than mobile robots, NVIDIA offers a separate platform, **Metropolis**, for vision-AI tasks like retail analytics and factory inspection; it targets the same Jetson hardware but a different use case.[24]

Higher up the stack, [NVIDIA Isaac Sim](/wiki/nvidia_isaac_sim) provides a physics-accurate simulator, built on NVIDIA's Omniverse platform, where robot policies can be tested before they touch real hardware, and Isaac Lab builds on it with a GPU-parallelized framework for training policies with [reinforcement learning](/wiki/reinforcement_learning) at scale, running many simulated robots in parallel to shrink training time and narrow the [sim-to-real transfer](/wiki/sim_to_real_transfer) gap. Isaac GR00T sits at the top of the stack as NVIDIA's family of open [foundation models](/wiki/foundation_model) for humanoid manipulation and locomotion, trained in simulation and from [imitation learning](/wiki/imitation_learning) on human demonstration data, then deployed onto Jetson hardware for on-robot inference.[25] NVIDIA frames the whole chain, Isaac Sim and Isaac Lab for training, Isaac ROS for deployment, and Jetson for on-robot inference, as one connected pipeline rather than a set of separate products.[25]

## Use in humanoid and mobile robots

A humanoid robot's onboard computer has to do two very different jobs on the same board: heavy, parallel perception and planning work, interpreting camera, [lidar](/wiki/lidar), and [inertial measurement unit](/wiki/inertial_measurement_unit) data and running a model to decide what to do next, alongside fast, deterministic control loops that turn those decisions into motor commands many times per second, where a delay can mean a fall. Jetson's GPU handles the first job and its CPU cores and dedicated accelerators handle the second, on one shared board rather than two separate computers linked by a cable.[26]

NVIDIA's own May 2026 open humanoid reference design illustrates how far that integration has come: a research robot built on a Unitree H2 Plus chassis and [Sharpa](/wiki/sharpa) Wave tactile five-finger hands, with 75 total [degrees of freedom](/wiki/degrees_of_freedom) across body and hands, runs entirely from a single Jetson AGX Thor T5000 module. NVIDIA developed the design with Ai2, ETH Zurich, Stanford's Robotics Center, and UC San Diego's Advanced Robotics and Controls Laboratory for academic research, with [Unitree](/wiki/unitree) planning to ship it in late 2026.[27]

Adoption is not limited to Jetson's newest chip. Warehouse and delivery robots, agricultural equipment, and quadrupeds still ship largely on Orin-generation modules, which remain in production. NVIDIA's own product lifecycle listing shows Orin Nano, Orin NX, and AGX Orin available to order through January 2032, giving integrators roughly a decade of supply certainty from a single design choice. Xavier-generation modules remain available through July 2027, and the original Jetson Nano is scheduled to phase out in January 2027.[28]

## Alternatives to Jetson

Jetson is not the only way to put AI compute inside a robot, though independent reviewers and trade press generally describe it as the default choice for teams that are not building their own chips.[2][14] The most visible alternative is vertical integration: [Tesla](/wiki/tesla) designs custom silicon for its [Optimus](/wiki/tesla_optimus) humanoid and its vehicles rather than buying Jetson modules, on the reasoning that a chip co-designed with Tesla's own neural networks and produced at automotive volume can outperform a general-purpose part. Tesla taped out a successor chip, [AI5](/wiki/tesla_ai5), in April 2026; Elon Musk said at the time that it would deliver roughly 5 times the useful compute (around 8 times the raw compute) of Tesla's current in-house hardware, with volume production targeted for mid-to-late 2027 and first use in Optimus and Tesla's own compute clusters.[29] That kind of vertical integration only pays off at a scale of millions of units a year, which is part of why smaller humanoid developers have generally chosen Jetson's off-the-shelf modules and software over the cost of designing a chip from scratch.

In China, where a large share of the world's humanoid-robot manufacturing volume is now based, tightening restrictions on exports of advanced NVIDIA chips are pushing some manufacturers toward domestic accelerators such as Huawei's Ascend line. Analysts at Goldman Sachs said in a May 2026 note, reported by CNBC, that this shift toward domestic silicon in China is likely to accelerate through 2028.[30] How far that substitution extends to Jetson specifically, as opposed to the larger training chips that export controls target most directly, is not yet clear as of mid-2026.

## See also

- [Jetson Thor](/wiki/jetson_thor)
- [NVIDIA](/wiki/nvidia)
- [NVIDIA Isaac Sim](/wiki/nvidia_isaac_sim)
- [Isaac Lab](/wiki/isaac_lab)
- [Isaac GR00T](/wiki/isaac_gr00t)
- [Edge AI](/wiki/edge_ai)
- [Humanoid robot](/wiki/humanoid_robot)
- [Tesla AI5](/wiki/tesla_ai5)
- [GPU](/wiki/gpu)

## References

1. NVIDIA, "Jetson AGX Orin for Next-Gen Robotics," nvidia.com, accessed July 2026. https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/
2. The Robot Report, "How does NVIDIA's Jetson Thor compare with other robot brains on the market?" 2025. https://www.therobotreport.com/how-does-nvidias-jetson-thor-compare-with-other-robot-brains/
3. NVIDIA Newsroom, "NVIDIA Blackwell-Powered Jetson Thor Now Available, Accelerating the Age of General Robotics," August 25, 2025. https://nvidianews.nvidia.com/news/nvidia-blackwell-powered-jetson-thor-now-available-accelerating-the-age-of-general-robotics
4. NVIDIA Developer, "Jetson Modules, Support, Ecosystem, and Lineup," developer.nvidia.com, accessed July 2026. https://developer.nvidia.com/embedded/jetson-modules
5. NVIDIA Technical Blog, "Introducing NVIDIA Jetson Thor, the Ultimate Platform for Physical AI," August 25, 2025. https://developer.nvidia.com/blog/introducing-nvidia-jetson-thor-the-ultimate-platform-for-physical-ai/
6. Wikipedia, "Nvidia Jetson," accessed July 2026. https://en.wikipedia.org/wiki/Nvidia_Jetson
7. NVIDIA Developer, "Jetson Download Center" and related Jetson TX1/TX2 documentation, developer.nvidia.com, accessed July 2026. https://developer.nvidia.com/embedded/downloads
8. NVIDIA Technical Blog, "NVIDIA Jetson AGX Xavier Delivers 32 TeraOps for New Era of AI in Robotics," 2018. https://developer.nvidia.com/blog/nvidia-jetson-agx-xavier-32-teraops-ai-robotics/
9. NVIDIA Newsroom, "NVIDIA Releases Jetson Xavier NX Developer Kit with Cloud-Native Support," 2019/2020. https://nvidianews.nvidia.com/news/nvidia-releases-jetson-xavier-nx-developer-kit-with-cloud-native-support
10. NVIDIA Newsroom, "NVIDIA Announces Jetson Nano: $99 Tiny, Yet Mighty NVIDIA CUDA-X AI Computer That Runs All AI Models," March 18, 2019. https://nvidianews.nvidia.com/news/nvidia-announces-jetson-nano-99-tiny-yet-mighty-nvidia-cuda-x-ai-computer-that-runs-all-ai-models
11. NVIDIA Newsroom, "NVIDIA Announces Availability of Jetson AGX Orin Developer Kit to Advance Robotics and Edge AI," March 22, 2022. https://nvidianews.nvidia.com/news/nvidia-announces-availability-of-jetson-agx-orin-developer-kit-to-advance-robotics-and-edge-ai
12. JetsonHacks, "NVIDIA Jetson Orin Nano Developer Kit: The Perfect Solution for Makers and Developers," March 2023. https://jetsonhacks.com/2023/03/22/nvidia-jetson-orin-nano-developer-kit-the-perfect-solution-for-makers-and-developers-a-review/
13. NVIDIA Technical Blog, "NVIDIA Jetson Orin Nano Developer Kit Gets a 'Super' Boost," December 2024. https://developer.nvidia.com/blog/nvidia-jetson-orin-nano-developer-kit-gets-a-super-boost/
14. DataCenterDynamics, "Nvidia launches Jetson Thor compute modules for humanoid robots," 2025. https://www.datacenterdynamics.com/en/news/nvidia-launches-jetson-thor-compute-modules-for-humanoid-robots/
15. CNBC, "Nvidia's 'robot brain' chip, Thor, goes on sale around the world," August 25, 2025. https://www.cnbc.com/2025/08/25/nvidias-thor-t5000-robot-brain-chip.html
16. NVIDIA Developer, "Deep Learning Accelerator (DLA)," developer.nvidia.com, accessed July 2026. https://developer.nvidia.com/deep-learning-accelerator
17. The Robot Report, "NVIDIA Jetson Thor brings 2K teraflops of AI compute to robots," 2025. https://www.therobotreport.com/nvidia-jetson-thor-brings-2k-teraflops-of-ai-compute-to-robots/
18. Boston Dynamics, "Boston Dynamics Expands Collaboration with NVIDIA to Accelerate AI Capabilities in Humanoid Robots," March 18, 2025. https://bostondynamics.com/news/boston-dynamics-expands-collaboration-with-nvidia/
19. 1X Technologies, "Inside 1X's Humanoid Robot Stack: Simulation, AI Training, and Onboard Compute with NVIDIA," March 17, 2026. https://www.1x.tech/discover/nvidia-gtc-2026
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21. Edge AI and Vision Alliance, "JetPack 7.2: The Production Moment for Physical AI," June 3, 2026. https://www.edge-ai-vision.com/2026/06/jetpack-7-2-the-production-moment-for-physical-ai/
22. NVIDIA Developer, "TensorRT SDK," developer.nvidia.com, accessed July 2026. https://developer.nvidia.com/tensorrt
23. NVIDIA Developer, "Isaac ROS," developer.nvidia.com, accessed July 2026. https://developer.nvidia.com/isaac/ros
24. NVIDIA, "Metropolis: Intelligent Vision AI for Smart Infrastructure," nvidia.com, accessed July 2026. https://www.nvidia.com/en-us/autonomous-machines/intelligent-video-analytics-platform/
25. NVIDIA Developer, "Isaac GR00T," developer.nvidia.com, accessed July 2026. https://developer.nvidia.com/isaac/gr00t
26. NVIDIA, "Use Case: NVIDIA Computing Platforms for Humanoid Robots," nvidia.com, accessed July 2026. https://www.nvidia.com/en-us/use-cases/humanoid-robots/
27. NVIDIA Newsroom, "NVIDIA Announces NVIDIA Isaac GR00T Reference Humanoid Robot for Academic Research," May 31, 2026. https://nvidianews.nvidia.com/news/nvidia-open-humanoid-robot-reference-design
28. NVIDIA Developer, "Jetson Product Lifecycle," developer.nvidia.com, accessed July 2026. https://developer.nvidia.com/embedded/lifecycle
29. Not a Tesla App, "Tesla's AI5 to Enter Production in 2H 2026, Rivals NVIDIA's $30K Chip in Performance," April 2026. https://www.notateslaapp.com/news/3519/teslas-ai5-to-enter-production-in-2h-2026-rivals-nvidias-30k-chip-in-performance
30. CNBC, "China learns to build without Nvidia," June 1, 2026. https://www.cnbc.com/2026/06/01/china-learns-to-build-without-nvidia.html