Indeed, but Intel and AMD are also upping their Python JIT game, and in the end Mojo code isn't portable anyway.

You always need to touch the hardware/platform APIs at some level, because even if the same code executes the same, the observed performance, or in the case of GPUs the numeric accuracy has visible side effects.

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melodyogonna an hour ago | parent [-]

It is portable in that you can write code to target multiple platforms in the same codebase. Mojo has powerful compile-time metaprogramming that allows you to tell the compiler how to specialise using a compile-time conditional, e.g. https://github.com/modular/modular/blob/9b9fc007378f16148cfa...

Of course, this won't be necessary in most cases if you're building on top of abstractions provided by Modular.

You don't get this choice using vendor-specific libraries; you're locked into this or that.

	▲	pjmlp an hour ago \| parent [-]
		Yes you do, you get PyTorch or whatever else, built on top of those vendor-specific libraries. That is the thing with Mojo, when it arrives as 1.0, the LLM progress and the investment that is being done in GPU JITs for Python, make it largely irrelevant for large scale adoption. Sure some customers might stay around, and keep Modular going, the gold question is how many.