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Part of the reason, I think, is that Qualcomm and Apple cut their teeth on mobile devices, and yeah wider SIMD is not at all a concern there. It's also possible they haven't even licensed SVE from Arm Holdings and don't really want to spend the money on it.

In Apple's case, they have both the GPU and the NPU to fall back on, and a more closed/controlled ecosystem that breaks backwards compatibility every few years anyway. But Qualcomm is not so lucky; Windows is far more open and far more backwards compatible. I think the bet is that there are enough users who don't need/care about that, but I would question why they would even want Windows in the first place, when macOS, ChromeOS, or even GNU/Linux are available.

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jovial_cavalier 5 hours ago | parent | prev [-]

A ton of vector math applications these days are high dimensional vector spaces. A good example of that for arm would I guess be something like fingerprint or face id.

Also, it doesn't just speed up vector math. Compilers these days with knowledge of these extensions can auto-vectorize your code, so it has the potential to speed up every for-loop you write.

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josefx 4 hours ago | parent [-]

> A good example of that for arm would I guess be something like fingerprint or face id.

So operations that are not performance critical and are needed once or twice every hour? Are you sure you don't want to include a dedicated cluster of RTX 6090 Ti GPUs to speed them up?

	▲	pertymcpert 33 minutes ago \| parent \| next [-]
		Those are extremely performance critical operations. A lot of people use their phone many times an hour.
	▲	jovial_cavalier 2 hours ago \| parent \| prev [-]
		I'd argue that those are actually very performance critical because if it takes 5 seconds to unlock your phone, you're going to get a new phone. The point is taken, though, that seemingly the performance is fine as it is for these applications. My point was only that you don't need to be running state of the art LLMs to be using vector math with more than 4 dimensions.