I've heard from a friend who works in the embedded space that Tensorflow Lite is still the only realistic (supported by vendors) game in town for running ML models on microcontrollers such as ESP32, nRF, etc. The hardware support listed for this project seems like it's targeting much "fatter" MCUs (Android, etc).

I am so confused by metas ecosystem. Perhaps others have the same issues. I have mountains of torchscript code. It worked fine for me - had no issues making the python compatible. Torchscript is now deprecated, and the ostensible replacement is torch.export and either: AOTInductor or executorch. torch.export is so limited - no control flow at runtime at all, less support of python than torchscript. It is far more work to hoist all the control flow out of the model than it ever was to make the model torchscript compatible. Feel like meta has moved on, but I'm still stuck in the past here.

It'd be great if it supports a wasm/web backend as well.

I bet a lot of trivial text capabilities (grammar checking, autocomplete, etc) will benefit from this rather than sending everything to a hosted model.

It's possible right now with onnx / transformers.js / tensorflow.js - but none of them are quite there yet in terms of efficiency. Given the target for microcontrollers, it'd be great to bring that efficiency to browsers as well.

I get the impression that https://github.com/pytorch/executorch is Meta’s take on TFLite / LiteRT, which is quite interesting.

While reading the README and related documentation, I noticed that Samsung Exynos NPU acceleration was listed, which immediately caught my attention. According to https://docs.pytorch.org/executorch/main/backends/samsung/sa..., Samsung has finally built and released an NPU SDK—so I followed the link to check it out.

Unfortunately, the experience was disappointing.

The so-called “version 1.0” SDK is available only for Ubuntu 22.04 / 20.04. There is no release date information per version, nor any visible roadmap. Even worse, downloading the SDK requires logging in. The product description page itself https://soc-developer.semiconductor.samsung.com/global/devel... does contain explanations, but they are provided almost entirely as images rather than text—presented in a style more reminiscent of corporate PR material than developer-facing technical documentation.

This is, regrettably, very typical of Samsung’s software support: opaque documentation, gated access, and little consideration for external developers. At this point, it is hard not to conclude that Exynos remains a poor choice, regardless of its theoretical hardware capabilities.

For comparison, Qualcomm and MediaTek actively collaborate with existing ecosystems, and their SDKs are generally available without artificial barriers. As a concrete example, see how LiteRT distributes its artifacts and references in this commit: https://github.com/google-ai-edge/LiteRT/commit/eaf7d635e1bc...

So the vulkan backend for pytorch is just in executorch?

I just want it on native desktop python.

How does performance stack up against TensorRT for edge NVidia hardware?

ExecuTorch member here.

- Better microcontroller support is in our roadmap for 2026. There is a lot of development happening here from support for Arduino, STMicro and others. We will do this openly with the community as usual so if you are interested, feel free to join our discord and looped into the github repo.

- Better web support is also in the roadmap. There is some limited support already though not sure exactly what your usecase is. Feel free to open up a GH issue and we can see if there is a way to unblock you.

- Will take the feedback about Samsung to them. Seeing the user feedback first hand here will likely help them prioritize some of that. This is partially why we have not called this a production ready backend unlike the other backends like Qualcomm, Vulkan and a few others we ourselves are using in production.