21.5-inch AI Touch Panel PC is powered by NVIDIA Jetson Orin NX module for industrial HMI applications

AAEON NIKY-2215-NX is a 21.5-inch Full HD AI Touch Panel PC powered by the NVIDIA Jetson Orin NX 8GB/16GB and designed for AI-enhanced HMI applications such as production line inspection systems and industrial monitoring dashboards. The panel PC features two GbE RJ45 jacks, four USB 3.2 Type-A ports, CAN Bus, RS-232/422/485, and DIO DB-9/15 connectors, and three M.2 sockets for NVMe storage, WiFI/Bluetooth, and 4G LTE/5G cellular connectivity. It can operate in a -5°C to +55°C temperature range, offers vibration and shock tolerance, and takes 12V to 24V DC input. AAEON NIKY-2215-NX specifications: SoM - NVIDIA Jetson Orin NX Jetson Orin NX 16GB – 8-core Arm Cortex-A78AE CPU, 1024-core NVIDIA Ampere GPU, up to 157 TOPS, 16GB LPDDR5 Jetson Orin NX 8GB – 6-core Arm Cortex-A78AE CPU, 1024-core NVIDIA Ampere GPU, up to 117 TOPS, 8GB LPDDR5 Storage - 128 GB (default) NVMe SSD via M.2 2280 M-Key socket Panel

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$4,290+ Unitree R1-A5 and R2-A7 humanoid robots features grippers or dexterous hands, fixed or wheeled base

Unitree has extended its R1 dual‑arm humanoid robot family with new R1-A5 and R1-A7 models, which can be fitted with 2-finger grippers or 3 or 5-finger dexterous hands, and attached to a fixed base or a wheeled base for indoor mobility. The new robots appear based on the low-cost Unitree R1 platform launched last year, which can dance, walk, run, perform kung-fu moves, and chat with users, but is otherwise not overly useful since it lacks dexterous hands. The R1-A5 and R1-A7 won't be able to dance, since they don't come with legs, but the upper body comes with a head and two arms equipped with grippers or dexterous hands, which could perform useful tasks in combination with binocular vision. Four new models are available with the following specifications: They mostly share the same specifications, but the R1-A7 has longer arms and adds 4 degrees of freedom (2 extra per

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NVIDIA phases out several Jetson modules due to high LPDDR4 RAM prices and tight supplies

Following the well-advertised Raspberry Pi 4/5 price hikes, we've just written an article about some SBCs quadrupling in price since 2024 due to RAM price increases, and another victim appears to be NVIDIA Jetson modules relying on LPDDR4 memory. That's according to a lifecycle update by Connect Tech that claims that "due to changes in global DRAM market dynamics, NVIDIA has indicated that supply and pricing for LPDDR4-based modules have become increasingly constrained. As a result, NVIDIA is accelerating End-of-Life (EOL) timelines for" specific modules. Four families are impacted: NVIDIA Jetson TX2 NX (4GB and 8GB) NVIDIA Jetson TX2i (all SKUs) NVIDIA Jetson AGX Xavier (32GB and Industrial) NVIDIA Jetson Xavier NX All these modules were released in 2021 or before. The end-of-life timeline is as follows: Now - All new purchase orders for products integrating TX2 NX, TX2i, AGX Xavier, and Xavier NX modules are Non-Cancelable, Non-Returnable (NCNR) July

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reBot Arm B601-DM – An open-source 6+1 DoF robotic arm for embodied AI and teleoperation applications

Seeed Studio reBot Arm B601-DM is a fully open-source 6-axis robotic arm (plus a parallel gripper) designed to lower the barrier to entry for embodied AI learning and teleoperation. Built around high-performance Damiao actuators, the arm offers up to 767mm of reach, a 1.5kg payload capacity, and high-precision 0.2mm repeatability. Designed for researchers and robotics developers, the B601-DM is compatible out of the box with major AI and robotics frameworks, including ROS 1/2, Hugging Face’s LeRobot, NVIDIA Isaac Sim, and Pinocchio. Seeed Studio reBot Arm B601-DM specifications: Communication – CAN bus @ 1Mbps) and UART @ 921600bps Degrees of Freedom (DOF) – 6-axis arm + 1 parallel gripper Motors / Actuators – 7x Damiao motors 4x DAMIAO 4310 (DM-J4310-2EC) 3x DAMIAO 4340P (DM-J4340P-2EC) high-torque motors Payload – 1.5kg (without gripper, at recommended 70% reach) Reach – 767mm (with gripper), 607mm (without gripper) Repeatability – < 0.2 mm Joint torque and

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IP65-rated TWOWIN T808P-G Edge AI computer features 8x GMSL2 cameras, aviation-grade M12/M16 connectors

TWOWIN Technology has introduced the TW-T808P-G, a rugged, fan-cooled, IP65 edge AI computer built around the NVIDIA Jetson Orin NX system-on-module (SoM). Designed specifically for autonomous driving, unmanned delivery vehicles, and smart inspection scenarios, the system provides up to 157 TOPS of AI performance and features various connectivity options for real-time processing in industrial and smart infrastructure environments. Housed in an aluminum alloy enclosure, the device features aviation-grade M12 and M16 connectors for Gigabit Ethernet (PoE), RS232/RS485 serial communication, and power. Other features include two FAKRA connectors for up to eight GMSL2 cameras, USB 3.0, CAN Bus, GPIO, HDMI, and M.2 NVMe storage. It also supports optional Wi-Fi, 4G/5G, and RTK GPS for centimeter-level positioning. With a wide 9–36V input, a zero-power standby design using automotive ACC (ignition) control, and an operating range of -40°C to +70°C, the system is designed for applications such as security monitoring, smart gas stations,

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Select the right hardware for your local LLM deployment with this online guide

When it comes to deploying local LLMs, many people may think that spending more money will deliver more performance, but it's far from reality.  That's why Sipeed created the "AI Agent Local LLM Inference Device Deployment Guide" hosted on the llmdev.guide website. The website lists common hardware with price, performance (tokens/s), power consumption, and more for various LLMs. If we take Qwen3.5 9B as an example, we can see that $4K+ hardware like NVIDIA DGX Spark or Apple Mac Studio  M3 delivers about the same TPS as a machine equipped with a $260 Intel Arc B580 12GB GPU. If money is no object and you'd like the best performance, the NVIDIA GTX 5090 32GB makes the most sense. I reckon the price comparison is imperfect because some data points reflect the price of a complete system, while others only list the price of a graphics card. However, for Qwen 122B-A10B,

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ADLINK DLAP-701 – An NVIDIA Jetson T5000/T4000 Edge AI platform for humanoid robots and vision sensing systems

ADLINK has just launched the DLAP-701 Series, a NVIDIA Jetson T5000/T4000-based compact edge AI platform designed for humanoid robots, autonomous mobile robots (AMR), and vision sensing systems (VSS). It supports up to 128GB LPDDR5X memory and features various I/O options, including dual Gigabit Ethernet, a QSFP port supporting 4×25GbE LAN, multiple USB 3.2 ports, and HDMI output, along with M.2 slots for Wi-Fi 6, 5G, and NVMe storage, as well as an mPCIe slot. It also integrates CAN-FD interfaces for robotics and vehicle control and TPM 2.0 security. With an operating voltage range of 9-36V DC and an industrial temperature range of -20°C to 60°C, it is designed for demanding edge environments. ADLINK DLAP-701 specifications Supported system-on-module – NVIDIA Jetson Thor T5000 or Jetson Thor T4000 Memory – Up to 128GB (T5000 variant), 64 GB (T4000 variant) 256-bit LPDDR5X (273 GB/s bandwidth) Storage 128GB SSD for the OS (most probably ADLINK's ASD+ industrial

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reComputer Jetson AGX Orin Developer Kit GMSL Bundle features 8x GMSL2 camera interfaces, 10GbE networking

Seeed Studio's reComputer Jetson AGX Orin Developer Kit GMSL bundle is an NVIDIA Jetson AGX Orin 32GB/64GB equipped with eight GMSL2 camera interfaces through two FAKRA connectors. It's based on the company's reComputer Mini J501 carrier board - a smaller version of the reServer Industrial J501 - with 10GbE and GbE RJ45 ports, HDMI 2.1 video output, two USB 3.2 ports, two USB 2.0 Type-C Debug/Recovery ports, and fitted with a 128 M.2 NVMe SSD and an M.2 WiFi 5 and Bluetooth 5.0 module, besides the daughterboard with the GMSL2 camera interfaces. reComputer Jetson AGX Orin Developer Kit GMSL Bundle specifications: Supported system-on-modules (one or the other) NVIDIA Jetson AGX Orin 32GB with CPU – 8-core Arm Cortex-A78AE v8.2 64-bit processor @ 2.2 GHz with 2MB L2 + 4MB L3 cache GPU and AI NVIDIA Ampere architecture with 1792 NVIDIA CUDA cores and 56 Tensor Cores @ 930 MHz DL Accelerator – 2x

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Lanner EAI-I351 – An NVIDIA Jetson Thor Edge AI computer with 100GbE QSFP28 port and 8x GMSL2 camera

Lanner EAI-I351 is a rugged edge AI computer built around the NVIDIA Jetson Thor platform, featuring a 100GbE QSFP28 port and 8x GMSL2 camera inputs for high-bandwidth networking and low-latency vision processing. The system supports up to 128 GB of 256-bit LPDDR5X memory, features up to four 25GbE lanes via a QSFP28 port, a 5GbE RJ45 port, HDMI 2.0, four USB 3.2 Gen1 ports, two RS232/422/485 serial interfaces with optional CANBus, digital I/Os, and audio interfaces. Three expansion sockets are offered with an M.2 NVMe slot, an M.2 E-Key for Wi-Fi/Bluetooth, and an M.2 B-Key for 4G LTE/5G cellular with dual Nano-SIM support. Designed for industrial deployments, the embedded computer can operate in a –25°C to 70°C temperature range and takes 24V/48V DC power input. It targets robotics, smart manufacturing, and autonomous systems such as collaborative robots, AMRs, and machine-vision-based factory automation. Lanner EAI-I351 specifications: Supported system-on-module - NVIDIA Jetson

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 boosts appreciated

genuinely, nvidia pisses me off SO much that I am willing to entirely switch platforms from this shit. is there any alternative to a nvidia jetson for using with ros for robotics-type applications? (example) I need hardware acceleration because I'm going to be:

  • doing SLAM on LiDAR data & several video streams (using RTAB-Map)
  • running some ML models on the video streams
  • hardware encoding of video streams (some of this has already been offloaded to dedicated devices, however I'm still going to need to do it for some other streams which don't use those devices)

willing to use something that isn't as dedicated to open source, so long as it's reasonably supported.
bonus points for open source, though

misc tags for visibility:
#nvidia #jetson #nvidiajetson #ros #ros2 #robotics #openrobotics