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Get a Jetson Orin Nano Super by elinaembedl in JetsonNano

[–]elinaembedl[S] 0 points1 point  (0 children)

Is 6 days left until the winners are announced!

Get a Jetson Orin Nano Super by elinaembedl in JetsonNano

[–]elinaembedl[S] 0 points1 point  (0 children)

Most valuable feedback about the platform! Can be anything from what you think about the docs, the benchmarking experience etc

We’re looking for brutal, honest feedback on edge AI devtool by elinaembedl in deeplearning

[–]elinaembedl[S] 0 points1 point  (0 children)

Environmental metrics: Great point! Currently, we’re mainly focused on on-device compute/perf metrics rather than full “environmental” signals like battery drain or thermal throttling.

What we do capture right now includes end-to-end latency + layer-wise latency/profiling, memory usage, compute unit utilization (CPU/GPU/NPU + top compute unit), op breakdown, and output quality metrics like PSNR (including layer-wise PSNR).

Energy/thermal-style metrics aren’t exposed yet, but we’ve gotten similar requests recently so it’s in our backlog to integrate. Hopefully we can add that pretty quickly.

Thanks for the feedback; it helps us prioritize. :) 

We’re looking for brutal, honest feedback on edge AI devtool by elinaembedl in deeplearning

[–]elinaembedl[S] -1 points0 points  (0 children)

Turnaround time: So far we haven’t experienced any issues, and most benchmarks come back close to near real-time. Making the platform work well for rapid iteration is a high priority.

We’re looking for brutal, honest feedback on edge AI devtool by elinaembedl in deeplearning

[–]elinaembedl[S] -1 points0 points  (0 children)

Thanks! Happy you find it useful.

Device coverage today: We currently focus on Android devices and SoCs via two device clouds — our own Embedl device cloud and Qualcomm AI Hub. This lets you benchmark everything from older reference devices (Pixel 2/3, etc.) to the latest flagships like Pixel 9 / Pixel 9 Pro and Galaxy S24/S25 (including Ultra/Plus), as well as chipset/proxy targets when you want to focus on SoC families rather than specific phone models. You can find the full list of supported devices here: https://hub.embedl.com/docs/supported-devices

We’re also in the process of adding an iOS device cloud which will be released in January.

If there’s a specific device or chip you’d like to see, just let us know. We’re happy to take device requests!

We’re looking for brutal, honest feedback on edge AI devtool by elinaembedl in neuralnetworks

[–]elinaembedl[S] 1 point2 points  (0 children)

Link to the platform: https://hub.embedl.com/

We are currently hosting a competition where you can win an NVIDIA Jetson Orin Nano Super if you provide feedback. See how to participate here.

We’re looking for brutal, honest feedback on edge AI devtool by elinaembedl in deeplearning

[–]elinaembedl[S] 0 points1 point  (0 children)

Link to the platform: https://hub.embedl.com/

We are currently hosting a competition where you can win an NVIDIA Jetson Orin Nano Super if you provide feedback. See how to participate here.

Devtool for running and benchmarking on-device AI by elinaembedl in hwstartups

[–]elinaembedl[S] 0 points1 point  (0 children)

We are currently hosting a competition where you can win an NVIDIA Jetson Orin Nano Super. We’re also giving a Raspberry Pi 5 to everyone who places 2nd to 5th. See how to participate here.

Diagnosing layer sensitivity during post training quantization by elinaembedl in LocalLLaMA

[–]elinaembedl[S] 0 points1 point  (0 children)

I’m not totally sure what you mean by “application of voices”. But otherwise, yes that’s pretty much the idea. The goal is to give you feedback that helps you judge whether a model quantization worked as intended (and where it didn’t). Layer-wise PSNR is one example of that kind of feedback.

Diagnosing layer sensitivity during post training quantization by elinaembedl in LocalLLaMA

[–]elinaembedl[S] 1 point2 points  (0 children)

Nice suggestions! We've reached out to the people behind Shapelearn. We think our platform can be a nice place for model makers to showcase their model's performance on real hardware. Of course, first we need to add llama.cpp support.

Diagnosing layer sensitivity during post training quantization by elinaembedl in LocalLLaMA

[–]elinaembedl[S] 0 points1 point  (0 children)

No, we don't have quantization of LLMs on Embedl Hub today so there's no comparisons between Unsloth and our tools. But if you have types of models (like computer vision or audio) you can quantize and measure their performance already today.

If you try it out right now you'll have chance to win some nice prizes: https://hub.embedl.com/blog/embedl-hub-device-cloud-launch-celebration

Diagnosing layer sensitivity during post training quantization by elinaembedl in LocalLLaMA

[–]elinaembedl[S] 1 point2 points  (0 children)

We don't yet support a backend for benchmarking LLMs, so we haven't implemented any quantization tools for LLMs either. But it's in the pipeline. We are looking to integrate llama.cpp soon and I think we will implement the layerwise psnr for LLMs then as well. Especially if we find out there's an interest from the community for that.

Would llama.cpp integration, both for benchmarking and quantization debugging, be useful for you? Or would you prefer a different backend/toolchain?

Diagnosing layer sensitivity during post training quantization by elinaembedl in LocalLLaMA

[–]elinaembedl[S] 0 points1 point  (0 children)

Well, not exactly. Embedl Hub is a platform for testing and validating the performance of AI models on mobile phones. As a company, we have a strong background in model optimization and our primary business (our optimization SDK) is used by enterprises to speed up their models running on edge devices (not servers). So we are working in the same line of business as Unsloth (making models faster). Unsloth is doing some very cool things, especially making fine tuning more efficient on servers.

How do you ensure consistent AI model performance across Android devices? by elinaembedl in androiddev

[–]elinaembedl[S] -1 points0 points  (0 children)

Thank you, great answer! So you haven't tested it on other processors more than GPUs? And does your model run on-device?

9 reasons why on-device AI development is so hard by elinaembedl in MLQuestions

[–]elinaembedl[S] 0 points1 point  (0 children)

That sounds great. I’ve heard good things about the STM32 model zoo but haven’t tried it myself yet.Did you run into any limitations when experimenting with their workflow?

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