container2wasm VS neural-engine

The existence of vscode.dev always makes me wonder why Microsoft never released an iOS version of VSCode to get more users into its ecosystem. Sure, it's almost as locked down as the web environment, but there's a lot of space in that "almost" - you could do all sorts of things like let users run their code, or complex extensions, in containers in a web view using https://github.com/ktock/container2wasm or similar.

Unfortunately cosmopolitan wouldn't work for dockerc. Cosmopolitan works as long as you only use it but container runtimes require additional features. Also containers contain arbitrary executables so not sure how that would work either...

As for WASM, this is already possible using container2wasm[0] and wasmer[1]'s ability to generate static binaries.

[0]: https://github.com/ktock/container2wasm

[1]: https://wasmer.io/

Really impressed by the depth and breadth of this project, well done!

A particularly interesting part is the socket layer inside the browser. Other people solving this problem have previously used a proxy to a server that does the real socket implementation. This means you can't have a "browser-only" solution.

The author has solved this (for HTTP/S only) by proxying HTTP requests and then re-creating them as fetch requests (details here: https://github.com/ktock/container2wasm/tree/main/examples/n...). I'm very interested in using this approach for my own project Runno (https://runno.dev).

~38 TOPS at fp16 is amazing, if the quoted number if fp16 (ANE is fp16 according to this [1] but that honestly seems like a bad choice when people are going smaller and smaller even at the higher level datacenter cards so not sure why apple would use it instead of fp8 natively)

[1]: https://github.com/hollance/neural-engine/blob/master/docs/1...

yep. they have a neural engine that is separate from the CPU and GPU that does really fast matmuls https://github.com/hollance/neural-engine. it's basically completely undocumented.

Looks like the ANE ("Apple Neural Engine") cores are powerful but not as flexible/programmable as the GPU cores. There is no sign that LLM inference is possible with them or ever will be unless Apple either opens up the closed ANE software framework for extensibility or they extend the ANE framework to support modern LLMs themselves. I would not hold my breath.

If Apple would wake up to what's happening with llama.cpp etc then I don't see such a big role for paying for remote access to big models via API

Currently a Macbook has a Neural Engine that is sitting idle 99% of the time and only suitable for running limited models (poorly documented, opaque rules about what ops can be accelerated, a black box compiler [1] and an apparent 3GB model size limit [2])

OTOH you can buy a Macbook with 64GB 'unified' memory and a Neural Engine today

If you squint a bit and look into the near future it's not so hard to imagine a future Mx chip with a more capable Neural Engine and yet more RAM, and able to run the largest GPT3 class models locally. (Ideally with better developer tools so other compilers can target the NE)

And then imagine it does that while leaving the CPU+GPU mostly free to run apps/games ... the whole experience of using a computer could change radically in that case.

I find it hard not to think this is coming within 5 years (although equally, I can imagine this is not on Apple's roadmap at all currently)

[1] https://github.com/hollance/neural-engine

My question too. This semi-answer on the page seems to contradict itself (source: https://github.com/hollance/neural-engine/blob/master/docs/p... ):

"> Can I program the ANE directly?

Unfortunately not. You can only use the Neural Engine through Core ML at the moment.

There currently is no public framework for programming the ANE. There are several private, undocumented frameworks but obviously we cannot use them as Apple rejects apps that use private frameworks.

(Perhaps in the future Apple will provide a public version of AppleNeuralEngine.framework.)"

The last part links to this bunch of headers:

https://github.com/nst/iOS-Runtime-Headers/tree/master/Priva...

So might it be more accurate to say you can program it directly, but won't end up with something that can be distributed on the app store?