NVIDIA Jetson Orin Nano Developer Kit Gets a “Super” Boost
The
generative AI
landscape is rapidly evolving, with new
large language models
(LLMs), visual language models (VLMs), and vision language action (VLA) models emerging daily. To stay at the forefront of this transformative era, developers need a platform powerful enough to seamlessly deploy the latest models from the cloud to the edge with optimized inferencing and open ML frameworks using CUDA.
To support emerging applications for generative AI workloads in robotics and multimodal agents, NVIDIA is refreshing the
NVIDIA Jetson Orin Nano Developer Kit
through a software update with an unprecedented boost in performance of up to 1.7x at an amazing new price of $249. In recognition of the superb boost in performance and accessibility of advanced AI, the Jetson Orin Nano Developer Kit is being renamed as the NVIDIA Jetson Orin Nano Super Developer Kit.
Jetson Orin Nano Developer Kit can be upgraded to Jetson Orin Nano Super Developer Kit with just a software update.
In this post, learn about the new capabilities of the developer kit and how you can seamlessly tap into the boosted performance.
The new Jetson Orin Nano Super Developer Kit
The
Jetson Orin Nano Super Developer Kit
now offers the following improvements:
1.7x higher generative AI model performance.
67 Sparse TOPs, a significant increase from the previous 40 Sparse TOPs
102 GB/s of memory bandwidth, a significant leap from the previous 65 GB/s memory bandwidth.
1.7 GHz of CPU clock speed, up from 1.5 GHz.
New reduced price of $249, down from $499.
Video 1. Introducing NVIDIA Jetson Orin Nano Super: The World’s Most Affordable Generative AI Computer
With the same hardware architecture, this performance boost is enabled by a new power mode which increases the GPU, memory, and CPU clocks. All previous Jetson Orin Nano Developer Kits can use the new power mode by upgrading to the latest version of
JetPack
.
With this performance boost, the Jetson Orin Nano Super Developer Kit delivers up to 70% higher generative AI performance, enabling you to run an emerging class of newer Transformer-based models. This compact yet powerful system can effortlessly handle a wide range of LLMs, VLMs, and Vision Transformers (ViTs), from smaller models to those with up to 8B parameters, such as the Llama-3.1-8B model.
Jetson supports the same ML frameworks and optimized inferencing infrastructure as other platforms, such as HuggingFace Transformers, Ollama, llama.cpp, vLLM, MLC, NVIDIA TensorRT-LLM, and more. This makes it an ideal choice for you to quickly adapt and deploy scalable solutions between the cloud, edge, and PC.
Jetson Orin Nano Super Developer Kit configuration
NVIDIA Jetson Orin Nano Developer Kit
(original)
NVIDIA Jetson Orin Nano Super Developer Kit
GPU
NVIDIA Ampere architecture
1,024 CUDA Cores
32 Tensor Cores
635 MHz
NVIDIA Ampere architecture
1,024 CUDA Cores
32 Tensor Cores
1,020 MHz
AI PERF
40 INT8 TOPS (Sparse)
20 INT8 TOPS (Dense)
10 FP16 TFLOPs
67 TOPS (Sparse)
33 TOPS (Dense)
17 FP16 TFLOPs
CPU
6-core Arm Cortex-A78AE v8.2 64-bit CPU
1.5 GHz
6-core Arm Cortex-A78AE v8.2 64-bit CPU
1.7 GHz
Memory
8GB 128-bit LPDDR5
68 GB/s
8GB 128-bit LPDDR5
102 GB/s
MODULE POWER
7W | 15W
7W | 15W |
25W
Table 1. Jetson Orin Nano Super Developer Kit configuration comparison
Runs a wide range of LLMs, VLMs, and ViTs
The NVIDIA Jetson Orin Nano Super Developer Kit offers performance that is a game-changer if you want to bring generative AI capabilities to your embedded applications or experience cutting-edge generative AI models in an affordable computer.
LLMs, VLMs, and vision transformers (ViTs) are transformative AI architectures driving innovation across domains. Foundation LLMs excel at generalized language processing and generating human-like text, enabling natural human/machine interfaces when coupled with streaming speech recognition and synthesis pipelines such as
NVIDIA Riva
.
Having advanced rapidly to compete with larger models through techniques like
pruning and distillation
, small language models (SLMs) are optimized for edge use cases, and are generally referred to as being <7B parameters in size.
Today, it’s common for open LLMs and SLMs to have been trained for agentic tool use and function calling with structured I/O, which connects LLM parsing and reasoning to real-world systems.
Support for LLM fine-tuning and memory-efficient LoRAs have also improved across many ML frameworks, including
NVIDIA NeMo
, enabling well-understood recipes for the alignment and specialization of SLMs in application-specific domains.
ViTs harness the power of Transformers for vision by tokenizing patches of pixels into embeddings. They have achieved state-of-the-art performance across fundamental tasks:
Feature extraction
Classification
Detection
Segmentation
Pose estimation
They also extend to higher-dimensional modalities such as 3D point clouds and video with
NVIDIA Cosmos Tokenizers
. ViTs prompt creative new hybrid models that blend bespoke vision capabilities with open-vocabulary language support and dynamic runtime queries against a wide variety of subject matter and object classes, without needing additional fine-tuning.
VLMs such as
VILA
fuse ViTs and LLMs across visual and textual modalities, empowering models to understand and generate detailed scene descriptions, detecting objects of interest and extracting text with OCR. They can apply multimodal reasoning in response to user queries about images or video sequences.
Having undergone similar minification efforts to SLMs, VLMs are increasingly useful for edge applications when applied to the remote monitoring of camera streams with flexible event-based alerts and summarization, such as in the reference
VLM Workflow
in Jetson Platform Services.
Together, these technologies underpin advances in generative AI and multimodal systems, and real-world applications. The new NVIDIA Jetson Orin Nano Super delivers substantial performance increases on a wide variety of LLMs, SLMs, ViTs, and VLMs and significantly lowers the barrier of entry for gaining hands-on experience with the latest Transformer models as a gateway to physical AI. We benchmarked some of the popular LLMs, VLMs and ViTs below and showcase the speed up the Jetson Orin Nano Super developer kit provides over the predecessor.
Figure 1. LLM performance boost on Jetson Orin Nano Super Developer Kit
Model
Jetson Orin Nano
(original)
Jetson Orin Nano Super
Perf Gain (X)
Llama 3.1 8B
14
19.14
1.37
Llama 3.2 3B
27.7
43.07
1.55
Qwen2.5 7B
14.2
21.75
1.53
Gemma 2 2B
21.5
34.97
1.63
Gemma 2 9B
7.2
9.21
1.28
Phi 3.5 3B
24.7
38.1
1.54
SmolLM2
41
64.5
1.57
Table 2. Benchmark performance in tokens\sec for popular LLMs
* LLM generation performance (tokens per second) with INT4 quantization using MLC API.
Figure 2. VLM performance boost on NVIDIA Jetson Orin Nano Super Developer Kit
Model
Jetson Orin Nano (original)
Jetson Orin Nano Super
Perf Gain (X)
VILA 1.5 3B
0.7
1.06
1.51
VILA 1.5 8B
0.574
0.83
1.45
LLAVA 1.6 7B
0.412
0.57
1.38
Qwen2 VL 2B
2.8
4.4
1.57
InternVL2.5 4B
2.5
5.1
2.04
PaliGemma2 3B
13.7
21.6
1.58
SmolVLM 2B
8.1
12.9
1.59
Table 3. Benchmark performance in tokens\sec for popular VLMs
*All VILA and LLAVA models were run with INT4 precision using MLC while the rest of the models were run in FP4 precision with Hugging Face Transformers.
Figure 3. ViTs performance boost on NVIDIA Jetson Orin Nano Super Developer Kit
Model
Jetson Orin Nano (original)
Jetson Orin Nano Super
Perf Gain (X)
clip-vit-base-patch32
196
314
1.60
clip-vit-base-patch16
95
161
1.69
DINOv2-base-patch14
75
126
1.68
SAM2 base
4.42
6.34
1.43
Grounding DINO
4.11
6.23
1.52
vit-base-patch16-224
98
158
1.61
vit-base-patch32-224
171
273
1.60
Table 4. Benchmark performance in frames\sec for popular Vision Transformers
*All ViT models were run with FP16 precision using NVIDIA TensorRT (measurements are in FPS).
Getting started with Jetson Orin Nano Super Developer Kit
To enjoy the boosted performance, download the SD Card image from the
JetPack SDK page
and follow the
Getting Started Guide
.
NVIDIA released a SD card image based on JetPack 6.1 with support for boosted performance. You can also install JetPack 6.1 with boosted performance for Jetson Orin Nano Developer Kit using SDK Manager. Make sure that you have updated the SDK Manager and select JetPack 6.1 (rev. 1) while installing.
When you’re up and running with JetPack, change the power mode using the following command to unlock the super performance. Mode 2 is MAXN mode, which brings the super performance.
sudo nvpmodel -m 2
You can also change the power mode using the Power Mode Selector on the right side of the Ubuntu desktop’s top bar.
Figure 4. Power mode selection tool
Experience generative AI on Jetson Orin Nano Super Developer Kit
NVIDIA offers a range of tutorials and pre-built containers in the Jetson AI Lab for exploring generative AI on the Jetson Orin Nano Developer Kit. If you’re interested in robotics, be sure to explore the LeRobot tutorial. For those looking to create a generative AI chatbot, there’s a dedicated tutorial for building one.
Hugging Face LeRobot
NVIDIA has
partnered
with Hugging Face to accelerate robotic research on the LeRobot open AI platform. You can run
HuggingFace LeRobot on Jetson Orin Nano Super Developer Kit
, which runs generative AI models for predicting actions for a particular task from visual inputs and prior trajectories.
Figure 5. Hugging Face LeRobot running on Jetson Orin Nano Developer Kit
Generative AI–driven chatbot with Ollama
Run a
generative AI chatbot
on Jetson Orin Nano Super Developer Kit. This chatbot features
Ollama
with
Open WebUI
, a widely used, open-source, chatbot server interface that connects to locally running LLMs. It uses retrieval-augmented generation (RAG) to further enhance the user experience and capabilities.
Figure 6. Generative AI chatbot running on Jetson Orin Nano Super Developer Kit
Advancing generative AI with Jetson AI Lab
The
Jetson AI Lab
is the hub for discovering and experimenting with the latest generative AI technologies optimized for edge devices.
By fostering an openly collaborative and community-driven environment, NVIDIA works alongside developers and partners to advance open-source edge AI and robot learning. With our comprehensive support for popular machine learning frameworks and optimized inferencing microservices on Jetson devices, you can quickly build and deploy the latest research innovations and models on your Jetson computers to keep up with the rapid pace of innovation.
Figure 7 shows examples of the
NanoOWL
open-vocabulary real-time object detection ViT.
Figure 7. Real-time object detection vision example
Figure 8 shows the
NanoDB
multimodal vector database with interactive txt2img and img2img similarity search.
Figure 8. Image similarity search examples
Jetson AI Lab
offers almost 50 easy-to-follow tutorials and prebuilt containers to get developers of any experience level to quickly get started with on-device LLMs, SLMs, and multimodal VLMs, along with variants for VLAs, diffusion policies, and speech models. They’re all deployed locally with the same optimized inferencing infrastructure used in the cloud.
The collaborative, community-driven tutorials and resources of the
Jetson AI Lab
significantly reduces the barriers to entry for deploying advanced generative AI to the edge.
All Jetson Orin Nano series and Jetson Orin NX series modules gets a super performance boost
NVIDIA is also refreshing the performance across the Jetson Orin Nano series and Jetson Orin NX series. From small AI cameras to large autonomous machines, every edge device needs the compute capability to run generative AI models​.
With the same module hardware, you can take advantage of the up to 1.7x increased performance across the different modules.
Orin Nano series:
Improvements to the frequencies and performance on the GPU, CPU, and memory.
Orin NX series:
Improvements across the GPU and DLA.
Table 5. Jetson Orin Nano Super and Orin NX Super module specifications
Support and documentation for boosted performance for Jetson Orin Nano and Orin NX production modules will be released in the first half of January with JetPack 6.1.1.
Due to the growing customer demand for Orin, NVIDIA also
recently announced
the extension of the product lifecycle of Jetson Orin through 2032. With this super performance boost, the Orin Nano series and Orin NX series are the ideal platforms for both current and future models.
Jumpstart your generative AI developer today
The
Jetson Orin Nano Super Developer Kit
is your ultimate platform for leading the way in generative AI development for edge computing. Now is the perfect time to get started and join the vibrant and diverse community of advanced developers and researchers working together with NVIDIA to address real-world challenges with physical AI.
For existing Jetson Orin Nano Developer Kit users, upgrade your
JetPack SDK
to unlock boosted performance today.
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NVIDIA Jetson Orin Nano 开发者套件得到“超级”提升
生成式 AI
格局正在迅速发展，新的
大语言模型 (LLMs)
、视觉语言模型 (VLMs) 和视觉语言动作 (VLAs) 模型每天都在涌现。为了在这个变革时代站在前沿，开发者需要一个足够强大的平台，通过使用 CUDA 的优化推理和开放 ML 框架，将最新模型从云端无缝部署到边缘。
为了支持机器人和多模态智能体中生成式 AI 工作负载的新兴应用，NVIDIA 正在通过软件更新来更新
NVIDIA Jetson Orin Nano 开发者套件
，以 249 美元的惊人新价格将性能提升高达 1.7 倍。为表彰 Jetson Orin Nano 开发者套件在性能和可访问性方面的出色提升，我们将 Jetson Orin Nano 开发者套件更名为 NVIDIA Jetson Orin Nano Super Developer Kit。
只需更新软件，即可将 Jetson Orin Nano 开发者套件升级到 Jetson Orin Nano 超级开发者套件。
在本文中，了解开发者套件的新功能，以及如何无缝利用提升的性能。
全新 Jetson Orin Nano 超级开发者套件
Jetson Orin Nano
超级开发者套件
现在提供以下改进：
生成式 AI 模型性能提升 1.7 倍。
67 Sparse TOPs 的数量显著增加，较之前的 40 Sparse TOPs 有了明显提升
102 GB/s 的显存带宽，与之前的 65 GB/s 显存带宽相比，实现了重大飞跃。
CPU 主频为 1.7 GHz，高于 1.5 GHz。
新降价 249 美元，低于 499 美元。
视频 1、隆重推出 NVIDIA Jetson Orin Nano Super：世界上最具经济实惠的生成式 AI 计算机
在相同的硬件架构下，这种性能提升是通过增加 GPU、内存和 CPU 时钟的新电源模式实现的。之前的所有 Jetson Orin Nano 开发者套件都可以通过升级到最新版本的
JetPack
来使用新的功率模式。
借助这种性能提升，Jetson Orin Nano 超级开发者套件可提供高达 70% 的生成式 AI 性能，使您能够运行新兴的基于 Transformer 的新型模型。从较小的模型到具有多达 8B 参数的模型 (例如 Llama-3.1-8B 模型)，这款紧凑而强大的系统可以轻松处理各种 LLM、VLM 和 Vision Transformers (ViTs)。
Jetson 支持与其他平台相同的 ML 框架和优化的推理基础设施，例如 Hugging Face Transformers、Llama.cpp、vLLM、MLC、NVIDIA TensorRT-LLM 等。这使其成为在云、边缘和 PC 之间快速调整和部署可扩展解决方案的理想选择。
JETSON Orin Nano Super Developer Kit 配置
NVIDIA Jetson Orin Nano 开发者套件
(原件)
NVIDIA Jetson Orin Nano 超级开发者套件
GPU
NVIDIA Ampere 架构，1,024 个 CUDA Cores，32 个 Tensor Cores，635 MHz
NVIDIA Ampere 架构，1,024 个 CUDA Cores，32 个 Tensor Cores，
1,020 MHz
AI 性能
40 INT8 TOPS (Sparse) 20 INT8 TOPS (Dense) 10 FP16 TFLOPs
67 TOPS (稀疏)
33 TOPS (密集)
17 FP16 TFLOPS
CPU
6 核 Arm Cortex-A78AE v8.2 64 位 CPU
1.5 GHz
6 核 Arm Cortex-A78AE v8.2 64 位 CPU
1.7 GHz
显存
8GB 128 位 LPDDR5
68 GB/秒
8GB 128 位 LPDDR5
102 GB/秒
模组功率
7 瓦|15 瓦
7 瓦|15 瓦|
25 瓦
表 1、Jetson Orin Nano 超级开发者套件配置对比
可运行各种 LLM、VLM 和 ViT
如果您想将生成式 AI 功能引入嵌入式应用程序或在经济实惠的计算机上体验先进的生成式 AI 模型，NVIDIA Jetson Orin Nano 超级开发者套件可提供颠覆性性能。
LLMs、VLMs 和视觉 Transformers (ViTs) 是推动跨领域创新的变革性 AI 架构。Foundation LLMs 擅长通用语言处理和生成类似人类的文本，在与流式语音识别和合成流程 (例如
NVIDIA Riva
) 结合使用时，可实现自然的人类/机器界面。
小语言模型 (SLMs) 通过
剪枝和蒸馏
等技术取得了快速发展，可以与更大的模型竞争，并针对边缘用例进行了优化，一般来说，参数大小在 7 亿以下。
如今，开放式 LLM 和 SLM 已通过结构化 I/O (将 LLM 解析和推理连接到真实系统) 接受代理工具使用和函数调用训练，这已成为常态。
许多 ML 框架 (包括
NVIDIA NeMo
) 也改进了对 LLM 微调和内存高效型 LoRAs 的支持，实现了在应用特定领域中对 SLM 进行对齐和专门化的易于理解的方法。ViTs 通过将像素片标记为嵌入，利用 Transformers 的强大功能实现视觉效果。它们在以下基本任务中实现了出色的性能：
特征提取
分类
检测
分割
姿态估计
它们还扩展到更高维的模式，例如 3D 点云和采用
NVIDIA Cosmos Tokenizers
的视频。ViTs 提示创意新型混合模型，该模型将定制视觉功能、开放词汇语言支持以及针对各种主题和对象类别的动态运行时查询融合在一起，无需额外微调。VLMs，例如
VILA
，融合 ViTs 和 LLMs 跨视觉和文本模式，使模型能够理解和生成详细的场景描述、检测感兴趣的物体并使用 OCR 提取文本。它们可以应用多模态推理来回应用户对图像或视频序列的查询。
在经历了与 SLMs 类似的精简工作后，VLMs 在应用于摄像头流的远程监控时（例如在 Jetson Platform Services 中的
参考 VLM Workflow
中）具有灵活的基于事件的警报和汇总功能，因此对于边缘应用的用途越来越大。
这些技术共同支撑着生成式 AI 和多模态系统以及现实世界应用的进步。新的 NVIDIA Jetson Orin Nano Super 可显著提升各种 LLM、SLM、ViT 和 VLM 的性能，并显著降低入门门槛，以便获得使用最新的 Transformer 模型作为物理 AI 门户的实践经验。我们在下方对一些热门的 LLM、VLM 和 ViT 进行了基准测试，并展示了 Jetson Orin Nano Super 开发者套件相较于前代产品所提供的加速。
图 1、Jetson Orin Nano 超级开发者套件的 LLM 性能提升
模型
Jetson Orin Nano
(原件)
Jetson Orin Nano Super
性能增益 (X)
Llama 3.1 8B
14
19.14
1.37
Llama 3.2 3B
27.7
43.07
1.55
Qwen2.5 7B
14.2
21.75
1.53
Gemma 2 2B
21.5%
34.97
1.63
Gemma 2 9B
7.2
9.21
1.28
Phi 3.5 3B
24.7
38.1
1.54
SmolLM2
41
64.5%
1.57
表 2、使用 MLC API 进行 INT4 量化，对热门 LLM 生成性能（每秒令牌数）进行基准测试
。
图 2、NVIDIA Jetson Orin Nano 超级开发者套件的 VLM 性能提升
模型
Jetson Orin Nano (原图)
Jetson Orin Nano Super
性能增益 (X)
VILA 1.5 3B
0.7
1.06
1.51
VILA 1.5 8B
0.574
0.83
1.45
LLAVA 1.6 7B
0.412
0.57
1.38
Qwen2 VL 2B
2.8
4.4
1.57
InterVL2.5 4B
2.5
5.1
2.04
PaliGemma2 3B
13.7
21.6%
1.58
SmolVLM 2B
8.1
12.9
1.59
表 3、对热门 VLM 进行性能基准测试 (令牌/秒) 所有 VILA 和 LLAVA 模型均使用 MLC 以 INT4 精度运行，而其余模型则使用 Hugging Face Transformers 以 FP4 精度运行。
图 3、NVIDIA Jetson Orin Nano 超级开发者套件上的 ViTs 性能提升
模型
Jetson Orin Nano (原图)
Jetson Orin Nano Super
性能增益 (X)
clip-vit-base-patch32
196
314
1.60
clip-vit-base-patch16
95%
163
1.69
DINOv2-base-patch-14
75
126
1.68
SAM2 base
4.42
6.34
1.43
Grounding DINO
4.11
6.23
1.52
vit-base-patch16-224
98
158
1.61
vit-base-patch32-224
174
273
1.60
表 4、主流 Vision Transformer 的基准性能 (以帧 秒为单位)
所有 ViT 模型均使用 NVIDIA TensorRT 以 FP16 精度运行 (测量以 FPS 为单位)。
Jetson Orin Nano Super Developer Kit 入门
要享受提升的性能，请从
JetPack SDK 页面
下载 SD 卡镜像，并按照
Getting Started Guide 操作
。
NVIDIA 基于 JetPack 6.1 发布了 SD 卡镜像，并支持提升性能。您还可以使用 SDK Manager 安装 JetPack 6.1，并提升 Jetson Orin Nano Developer Kit 的性能。请确保您已更新 SDK Manager 并选择 JetPack 6.1 (rev. 1) 安装时。
启动并运行 JetPack 后，请使用以下命令更改电源模式，以解锁超强性能。模式 2 是 MAXN 模式，可带来超强性能。
sudo nvpmodel -m 2
您还可以使用 Ubuntu 桌面顶部栏右侧的 Power Mode Selector 更改电源模式。
图 4、电源模式选择工具
在 Jetson Orin Nano 超级开发者套件上体验生成式 AI
NVIDIA 在 Jetson AI 实验室中提供一系列教程和预构建容器，用于探索 Jetson Orin Nano 开发者套件中的生成式 AI。如果您对机器人感兴趣，请务必查看 LeRobot 教程。对于希望创建生成式 AI 聊天机器人的用户，我们提供了一个专门的教程来构建生成式 AI 聊天机器人。
Hugging Face 乐机器人
NVIDIA 与 Hugging Face 合作
，在 LeRobot 开放式 AI 平台上加速机器人研究。您可以在 Jetson Orin Nano 超级开发者套件上运行 HuggingFace LeRobot，该套件运行生成式 AI 模型，用于根据视觉输入和先前的轨迹预测特定任务的动作。
图 5、Hugging Face LeRobot 运行在 Jetson Orin Nano 开发者套件上
OLlama 的生成式 AI 驱动聊天机器人
在 Jetson Orin Nano 超级开发者套件上运行生成式 AI 聊天机器人。此聊天机器人采用 Open WebUI 的
Ollama
，以 Open WebUI 的形式呈现，一个广泛使用的开源聊天机器人服务器接口，可连接到本地运行的 LLM。它使用检索增强生成 (RAG) 进一步增强用户体验和功能。
图 6、在 Jetson Orin Nano 超级开发者套件上运行的生成式 AI 聊天机器人
借助 Jetson AI 实验室推进生成式 AI 发展
Jetson AI Lab 是发现和试验针对边缘设备优化的最新生成式 AI 技术的中心。
通过营造公开协作和社区驱动的环境，NVIDIA 与开发者和合作伙伴携手推进开源边缘 AI 和机器人学习。我们全面支持 Jetson 设备上的热门机器学习框架和优化的推理微服务，因此您可以在 Jetson 计算机上快速构建和部署最新的研究创新和模型，以跟上创新的步伐。
图 7 显示了
NanoOWL
开放词汇实时物体检测 ViT 示例。
图 7. 实时物体检测视觉示例
图 8 显示了包含交互式 txt2img 和 img2img 相似性搜索的
NanoDB
多模态向量数据库。
图 8、图像相似度搜索示例
Jetson AI Lab
提供近 50 个易于学习的教程和预构建的容器，可帮助任何经验水平的开发者快速开始使用设备上的 LLM、SLM 和多模态 VLM，以及 VLA、扩散策略和语音模型的变体。它们均在本地部署，并与云端使用的优化推理基础架构相同。
Jetson AI Lab 的协作式社区驱动教程和资源
可显著降低在边缘部署高级生成式 AI 的门槛。
所有 Jetson Orin Nano 系列和 Jetson Orin NX 系列模组均可实现超强性能提升
NVIDIA 还更新了 Jetson Orin Nano 系列和 Jetson Orin NX 系列的性能。从小型 AI 摄像头到大型自主机器，每台边缘设备都需要具备运行生成式 AI 模型所需的计算能力。
借助相同的模组硬件，您可以充分利用不同模组性能提升高达 1.7 倍的优势。
Orin Nano 系列：
改进 GPU、CPU 和内存的频率和性能。
Orin NX series：
跨 GPU 和 DLA 的改进。
表 5、Jetson Orin Nano Super 和 Orin NX Super 模组规格
有关提升 Jetson Orin Nano 和 Orin NX 生产模组性能的支持和文档将于 1 月上半月发布，并支持 JetPack 6.1.1。
鉴于客户对 Orin 的需求日益增长，NVIDIA
最近还宣布
将 Jetson Orin 的产品生命周期延长至 2032 年。凭借这种超强性能提升，Orin Nano 系列和 Orin NX 系列成为当前和未来模型的理想平台。
立即启动您的生成式 AI 开发者
Jetson Orin Nano 超级开发者套件
是引领边缘计算生成式 AI 开发的终极平台。现在正是时候，您可以加入充满活力且多元化的高级开发者和研究人员社区，与 NVIDIA 携手合作，利用物理 AI 应对现实世界的挑战。
对于现有的 Jetson Orin Nano 开发者套件用户，请立即升级
JetPack SDK
以解锁性能提升。
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