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I think a better comparison is to look at floating point performance. For example, the 10 core M2 GPU does 3.6 TFLOPS (FP32) while an RTX 4060 does 15 TFLOPS and an RTX 4090 82.58
While FP32 non tensor flops at least looks comparable, FP16/BF16 with tensor core(nowadays a default for any neural network including LLM) at 330 TFlops/s blows away M2.
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It's not that simple though though. I have never got through an entire day with an iPhone (XR, 12, 13 Pro). I'm just hitting 2 days easily with my Pixel 7a with the same crap on it. My daughter just took an iPhone 15 back because it won't get through the day.
Hmm, interesting. I have the opposite with my 12. We must have quite different use patterns.
It’s amazing how bad the competition is. The A17 pro has 2 performance cores and 4 efficiency cores. The Google G3 has 9 cores of 3 different types, the fastest being slower than Apple’s performance cores, the most efficient being less efficient than apple’s efficiency cores. And it’s a phone so you don’t take advantage of the extra parallelism. You just get the worst of both worlds. no wonder these android phones ha…
A big part of Apple's "winning formula" is taking their giant piles of money and negotiating exclusive contracts for whatever is scheduled to be the most advanced semiconductor node next year. Anyone else literally cannot compete, they don't have billions in pocket change they don't know how to spend otherwise, so they'll have to wait until the exclusivity agreement expires.
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Metal owns a significantly bigger market for gaming revenue than DirectX.
Probably, but that's a completely different claim from your first, isn't it?
The OP said Apple should switch to DirectX. They can't because it's closed source. The reason given was that devs won't support 2 APIs. They already do. They support DirectX and Vulcan (PS5 version) almost always. And devs already support Metal - with more games than the latest DirectX.
Does the complex block in the diagram refer to complex numbers? That doesn't sound typical, does it? What type of work load that typically run on the GPU that would require complex numbers?
Does the complex block in the diagram refer to complex numbers? That doesn't sound typical, does it? What type of work load that typically run on the GPU that would require complex numbers?
Earlier quoted context omitted.
I think a better comparison is to look at floating point performance. For example, the 10 core M2 GPU does 3.6 TFLOPS (FP32) while an RTX 4060 does 15 TFLOPS and an RTX 4090 82.58
Unfortunately, hardly. Ampere's (Nvidia 3000 series), Ada's (Nvidia 4000 series), and RNDA 3's (AMD 7000 series) GPUs have doubled up their FP32 units in ways that differ in implementation (between AMD and Nvidia) but are relatively similarly poor in their ability to be utilized properly at rates much higher than pre-doubling (Nvidia is doing better than AMD in that, but very far from great). The formal TFLOPS compar…
Does the complex block in the diagram refer to complex numbers? That doesn't sound typical, does it? What type of work load that typically run on the GPU that would require complex numbers?