RidgeRun Announces Support for NVIDIA Jetson T2000 and T3000: Scalable AI for Robotics and Edge Applications

RidgeRun is expanding its NVIDIA Jetson support to include the newly announced Jetson Thor T2000 and T3000 modules.

Robotics and physical AI systems increasingly need to run computer vision, sensor processing, generative AI, and real-time decision-making directly at the edge. However, using high-performance hardware in commercial products can create challenges related to power consumption, cost, cooling, and physical size.

The new NVIDIA Jetson T2000 and T3000 modules address these challenges by bringing NVIDIA Blackwell architecture to more compact and efficient configurations. They extend the Jetson Thor family beyond high-end systems, making advanced AI more accessible for mainstream robotics and edge applications.

New NVIDIA Jetson T2000 and T3000 Modules

The Jetson T2000 and T3000 allow developers to select the performance level that best matches their application instead of overprovisioning compute and memory.

Specification	Jetson T2000	Jetson T3000
AI performance	400 FP4 TFLOPS	865 FP4 TFLOPS
GPU	Blackwell, 1,024 CUDA cores	Blackwell, 1,536 CUDA cores
CPU	6-core Arm Neoverse	8-core Arm Neoverse
Memory	16 GB LPDDR5X	32 GB LPDDR5X
Memory bandwidth	137 GB/s	273 GB/s
Power	40 W	70 W

Specifications are preliminary and subject to change.

The Jetson T3000 is designed for intelligent humanoid robots and autonomous systems moving toward large-scale production. It delivers similar inference performance for LLMs, VLMs, VLAs, and world foundation models, while operating at about half the size and power.

The Jetson T2000 is the entry point to the Thor family. Its 400 FP4 TFLOPS and 16 GB of memory make it suitable for visual AI agents, autonomous mobile robots, robotic manipulators, and other compact edge systems.

Key Benefits and Challenges they help solve

Scalable physical AI

The new modules help companies move from powerful prototypes to commercially deployable products. Teams can select the Jetson T2000, T3000, or a higher-performance Thor module according to their actual workload.

Better performance per watt

With 40 W and 70 W power targets, the Jetson T2000 and T3000 can reduce cooling, battery, and power-delivery requirements compared with higher-performance configurations.

Local AI processing Running models directly on the device reduces network dependency and supports faster responses. This is essential for robots and autonomous systems that must operate reliably even when cloud connectivity is limited.

Generative AI at the edge

The NVIDIA Blackwell architecture enables applications involving:

• Large language models

• Vision-language models

• Vision-language-action models

• World foundation models

• Visual and autonomous AI agents

The Jetson T3000’s 273 GB/s memory bandwidth is particularly valuable for transformer inference and multimodal workloads.

More cost-effective deployment

Not every application requires the Jetson T5000’s 128 GB of memory. The Jetson T2000 and T3000 reduce unnecessary hardware resources and can help lower production costs, particularly as memory prices increase.

However, teams should still evaluate model memory, thermal design, sensor bandwidth, and complete application performance. The advertised TFLOPS represent FP4 compute, while real results depend on quantization, model architecture, software optimization, and operating conditions.

What Applications and Industries are they for?

The Jetson T2000 and T3000 are designed for applications such as:

• Humanoid robotics: Multimodal perception, natural-language interaction, manipulation, and task planning.

• Autonomous mobile robots: Navigation, obstacle detection, localization, and intelligent task execution.

• Industrial automation: Robotic guidance, flexible assembly, visual inspection, and production monitoring.

• Robotic manipulation: Object recognition, pose estimation, grasp planning, and adaptive control.

• Visual AI agents: Real-time video understanding, event detection, search, and summarization.

• Smart infrastructure: Multi-camera analysis, safety monitoring, and intelligent facility systems.

• Autonomous equipment: Local perception and decision-making for logistics, agriculture, construction, and other industrial environments.

  
  

Developers can begin evaluating the modules using the NVIDIA Jetson AGX Thor Developer Kit. T3000 emulation will be available later this month with JetPack 7.2.1 release, while T2000 emulation is planned for a future release. Production availability is expected in Q1 2027.

What can RidgeRun do for you?

Selecting the right NVIDIA Jetson Thor module requires evaluating more than AI performance. Memory usage, sensor integration, power consumption, thermal design, and software optimization must work together as a complete system.

Jetson T2000 and T3000 come with different challenges, but RidgeRun is the right partner to help your engineering team in multiple tasks:

• BSP bring-up: RidgeRun can help bring up your custom hardware based on the Jetson T2000 or T3000, including boot, storage, networking, peripherals, and system validation.

• Yocto support and platform adaptation: We can adapt and maintain Yocto-based software for Jetson T2000 and T3000 platforms, including custom layers, recipes, images, and production configurations.

• Migration roadmap and consultancy: RidgeRun can evaluate your current platform and create a clear migration plan for moving to Jetson T2000 or T3000, including technical risks, effort, and recommended steps.

• Migration from Jetson T5000: We can help port and validate applications currently running on Thor T5000, reducing migration time and identifying compatibility or performance differences.

• Application performance benchmarking: We can benchmark your application on Jetson T2000 and T3000 to measure CPU, GPU, memory, video, AI, latency, and power performance.

• Custom camera driver development and porting: We can develop or port custom camera drivers, including support for V4L2, NVIDIA Argus, MIPI CSI-2 sensors, serializers, deserializers, and multi-camera systems.

• Camera and video pipeline integration: RidgeRun can integrate camera capture, ISP, encoding, decoding, display, recording, and streaming pipelines using GStreamer and NVIDIA multimedia technologies.

• AI model optimization: RidgeRun can optimize AI models for Jetson T2000 and T3000 using TensorRT, reduced precision, graph optimization, profiling, and hardware-specific acceleration.

• LLM and vLLM deployment and integration: RidgeRun can help deploy and integrate large language models on Jetson T2000 and T3000 for applications such as intelligent assistants, robotics, vision-language systems, and natural-language interfaces.

• Security feature bring-up: We can help enable and validate security features such as Secure Boot, disk encryption, encrypted storage, key provisioning, signed software, and trusted update mechanisms.

• Multimedia and streaming development: RidgeRun can build low-latency video streaming, recording, analytics, and remote-control applications using protocols such as RTSP, WebRTC, SRT, and RTP.

• Long-term engineering support: RidgeRun can provide ongoing support for maintenance, upgrades, bug fixes, performance improvements, and future software releases.

  
  

Contact us to learn more about NVIDIA Jetson T2000 , T3000 and how to engage with us: https://www.ridgerun.com/contact