| > I'm really curious as to why Apple has been unable to reproduce their leap in CPUs in the GPU space. GPUs are highly parallelized and specialized systems. The workloads are already being optimized for the GPU, rather than having a CPU which is being optimized to deal with more arbitrary workloads (with things like branch prediction, superscalar architecture, etc). So you could say, without creating new instructions to represent the workflow better, there is a fixed amount of digital logic needed to perform the given work, and that translates to a fixed amount of power draw needed on a particular fabrication process. So Apple could throw more transistors at the problem (with a memory bus that can support the extra need), but the same amount of work still would take the same amount of power and generate the same amount of heat. It is usually far easier and more efficient to create dedicated logic for particular common problems, such as certain ML operations or toward hardware video encoding/decoding. > It's not exactly surprising when Nvidia parts handily beat the M1/M2, but when both Qualcomm and Mediatek have better GPU performance _and_ efficiency [0] Benchmarks are highly subjective, so I'd wait for more reviews (preferably by people with more established reputations, and perhaps a website). Reviewers who might try to determine _why_ one platform is doing better than another. GPU benchmarks are even more so, because again the workloads are targeted toward the GPU, while the GPU is also optimized for handling particular workloads. This means that benchmarks can be apple-to-oranges comparisons - even before you find out that a given benchmark was optimized differently for different platforms. There is also of course the reality that some vendors will optimize their code for the benchmarks specifically, going as far as to overclock the chip or to skip requested instructions when a particular benchmark is detected. |