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Comment by eigenspace | Hacker News Reader

It's pretty interesting that Apple was so quick to get to industry-leading status on their CPUs, but their GPUs are still in a state where they won't match Nvidia's GPUs from 2024 until at least next year, and will need a significantly bigger chip to do so.

Similar story with Qualcomm but to a lesser degree (both on the CPU and GPU side). I wonder why that is.

Lack of priority? Legitimately harder problem to solve? Experience from mobile scaling to desktop differently than CPU experience?

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Apple (and the ARM ecosystem as a whole) has never really needed massive GPU compute before, it’s always been about power efficiency and just enough GPU oomph to make UI fluid. Even historic Mac Pro workloads never really needed tons of GPU prowess, the heaviest power users were primarily taxing video encode/decode and 2D raster effects (Adobe Suite etc) so that’s what they focused on. By contrast NVIDIA’s entire game has been raw GPU power for decades. M1 Max was really prescient in hindsight and has set the stage for, all things considered, Apple to be not as far behind as it could have been today.

I guess I just don't really buy your argument, because if their CPUs had turned out poorly, you could have applied the same argument to their CPUs instead of the GPUs.

Another user though pointed out that they didn't actually design the GPU until the A11, so perhaps it really is just a lack of in house experience.

> I guess I just don't really buy your argument, because if their CPUs had turned out poorly, you could have applied the same argument to their CPUs instead of the GPUs.

Doesn't the argument work fine on the CPU side? Apple doesn't seem to be hurting for lack of a Threadripper competitor.

No. It's always been about power efficiency. Apple's GPU are very good in that area just like the CPU.

https://arxiv.org/html/2502.05317v1

Apple vs. Oranges: Evaluating the Apple Silicon M-Series SoCs for HPC Performance and Efficiency

"Apple's M-series GPUs offer massive performance-per-watt, scaling efficiently from roughly 5W in base chips to around 40-50W in top-tier Max chips. This efficiency generally sits between 200 and 250 GFLOPS per Watt."

Sure. nVidia GPUs have higher performance. But they do it with 450W+ a.k.a. 10x the power

The M-series are ARM chips based on the A series from the iPhones. Therefore Apple had ten years of chip design (plus the prior decades of ARM history) to rely on. The GPUs and modems were a much more recent effort.

(My take. happy to be corrected by someone with chip design experience who can comment.)

The iPhones had GPUs too. As far as I'm aware, the GPU design in the M-series chips are directly descended from the iPhone GPU in the exact same way the CPU is.

Prior to the A11 chip they used PowerVR designed GPUs. They weren't done in house.

Ah I see, good to know.

Based on Alyssa Rosenzweig’s reverse engineering, the current GPUs are still descended from PowerVR IP

I would argue their architecture choices long term proved to be prescient (RISC, ARM, etc) and securing TSMC production capacity (beating competitors to the punch) aided them as well.

Their main architectural choice was not about "RISC" or "ARM" but it was choosing "Brainiac" over "Speed Demon", i.e. setting the goal to execute a great number of instructions per clock cycle at a moderate clock frequency, instead of executing a moderate number of instructions per clock cycle at a high clock frequency (the latter variant results in lower fabrication costs, which is why other companies were reluctant to pursue the same choice as Apple).

A high IPC is much easier to achieve when the instructions have a fixed-length encoding, so this RISC principle followed from their main choice.

ARM was chosen for the Newton, then the iPods, then the iPhone and so on. Apple’s experience with ARM has a long history. Same applies to RISC. Apple didn’t leave PPC because it was a dead-end. They went with Intel because IBM was not interested in developing a low power PPC just for Apple. IBM wasn’t (and still isn’t) even interested in developing a POWER chip for desktop workstations. If there are POWER machines that can be turned into workstations, it’s a side effect of them being targeted at entry-level half-rack systems.

Do you have comparisons for Apple’s GPU to NVIDIA’s at the same power and thermals?

Right now the M5 Max is one of the best laptop compute GPUs for blender rendering and doesn’t do too badly on raster tests either. But it operates at a fraction of the equivalent NVIDIA parts.

It’s more that Apple doesn’t provide a competing tier of device as the high end NVIDIA GPUs. But within the tiers they do provide they’re fairly competitive.

They want to be power efficient, while Nvidia doesn't. That means a much higher cost (larger chip area) for the same performance.

I don't buy this as an explanation for the gap in the actual silicon's performance.

Nvidia's chips lose very little performance when they're severely power limited. In fact, that's why Nvidia's Professional versions of their GPUs are typically running at under two-thirds of their consumer equivalent's power draw.

Nvidia's cards are primarily designed for their professional applications where the power consumption is lower, and then they just juice up the consumer cards deep into the territory of diminishing returns just so they win some benchmarks.

On a performance-per-watt basis, Apple is still behind an Nvidia card with the juiced up power consumption, and when you dial back the power draw, Nvidia cards are miles ahead of Apple's silicon for performance-per-watt.

> On a performance-per-watt basis, Apple is still behind an Nvidia card with the juiced up power consumption, and when you dial back the power draw, Nvidia cards are miles ahead of Apple's silicon for performance-per-watt.

Do you have a citation on Apple vs nVidia performance-per-watt? I'm not aware of any benchmark that shows nVidia with a better performance-per-watt. Higher performance, sure, but performance-per-watt, not that I'm aware of.

Another important thing to keep in mind here is the software. Nvidia has been optimizing their software stack for decades to squeeze every clock cycle out of the hardware under any limits. TensorRT alone does straight up magic. Apple is just starting out with MLX, and their hardware is often idling not because it is worse per watt but because the compiler does not know how to optimally load the ALU blocks for specific graphs yet

A GPU can be used for inference, but, for that use, there are much better choices. Apple designed their NPUs for that, IBM added an NPU to their mainframe chip and AMD and Intel are planning on adding inference-specific instructions to the amd64 ISA.

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