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NVIDIA Jetson Thor Developer Kit | Envi

Manuel Villanueva
Best Edge AI & Robotics Device by far
Great product, easy to setup and powerful.By far the best hardware for Edge AI and Robotics.Note that the device is not optimized to run Omniverse or IsaacLab.
4 personas la encontraron útil
Brayden Z
I LOVE ROBOTS
Runs o llama 70b instruct model perfectly. Had it casually talking to me within a couple hours out the box
Brook
Basically a cheaper DGX spark w/ different cluster capability
Good device, be prepared to navigate bios for setup. Its a dev device.
FFW
Incomplete disorganized documentation, SDK lackluster
Disclaimer: I know this is intended for robotics with companies like Anduril, Lockheed, Raytheon, etc. likely being the target demo but the marketing copy had me convinced this would be an easy button for running containers with CUDA and unified memory. Well, that’s only true if you like running one-off commands from CLI or using desktop GUIs. Don’t get your hopes up if you use containers, unless you love “docker run” syntax and don’t care about security.The documentation is all over the place. Important repos disappear from their site, for which you then have to find a random mirror some private citizen put on GitHub and pray it works. The SDK (Jetpack) and jetson-containers project are designed to wrap docker, NOT podman|systemd|kubernetes|etc. so if your preference is not docker or baremetal be prepared to suffer.“Jetson OS” (Linux for Tegra or L4T rebranded) is built on Ubuntu LTS 22.04 with so many outdated packages for container support - be prepared to build low level dependencies and common tool chains from source while spending hours stumbling through Nvidia’s forums to find a stray comment from an employee that offers a clue to resolving a permissions issue introduced by an undocumented security patch.Hey Nvidia I want a functional declarative container stack, not being limited to “docker run” please. dusty-nv’s “jetson-containers” repo is cool for finding prebuilt container images designed to work with the CUDA samples already in place for the T5000/Blackwell, but I don’t want to use DOCKER! Let alone run on bare metal... You need to improve general LXC support and keep the repos up to date with latest dependencies and toolchains. At the very least you should ship your own flavor of minikube or similar with jetson-containers bundled, to keep the official packages from getting trampled while us users hack around the broken pieces…I should be able to copy my existing declarative container configs (YAML|TOML|JSON, k8s for compose|quadlet|podlet, don’t care which) to this machine and run them with minimal adaption necessary. The Nvidia hooks/wrappers and syntax for using the CTK need to be collocated and/or config paths clearly documented in one KB page.Overall, if you buy this instead of a MINISFORUM/Beelink/Mac Studio, you’re paying a premium for CUDA support that’s only officially offered two ways: Baremetal or Docker (just Docker-anything else you’re on your own). The added complexity of ARM builds always being an afterthought for many projects is almost enough to make me wish I did a custom multi-4090 GPU x86 rig instead.P.S.Some announcement about who is taking over dusty-nv’s work and what the future of Jetson looks like would be nice.Ver másVer menos
17 personas la encontraron útil
Jeff S
Great for running LLMs
Jetson Thor performs very well and if you are happy building latest from source you get very good results with vllm.
3 personas la encontraron útil
Kim
Whack-a-mole!
Observed behavior:- usbipd “endless attach loop” on a BUSID is insufficient because BUSID changes when the USB identity changes.- Device may vanish from Connected list and remain only as Persisted, indicating the USB device is not enumerating on the host (cable/port/power management sensitivity).- Host component installs (Nsight Perf SDK) fail due to missing utilities (unzip) or symlink collisions (file exists), creating false “installation failed” signals unrelated to the device wipe objective.Ver másVer menos
6 personas la encontraron útil

Ver más reseñas

NVIDIA Jetson Thor Developer Kit

Sobre este producto

Reseñas

Best Edge AI & Robotics Device by far

I LOVE ROBOTS

Basically a cheaper DGX spark w/ different cluster capability

Incomplete disorganized documentation, SDK lackluster

Great for running LLMs

Whack-a-mole!

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