filisterApple argues in favor of selling Macs with only 8GB of RAM
Well yeah, they’re enough to meet the minimum use cases so they can upsell most people on expensive RAM upgrades.
That’s why I don’t buy laptops with soldered RAM. That’s getting harder and harder these days, but my needs for a laptop have also gone down. If they solder RAM, there’s nothing you can (realistically) do if you need more, so you’ll pay extra when buying so they can upcharge a lot. If it’s not soldered, you have a decent option to buy RAM afterward, so there’s less value in upselling too much.
So screw you Apple, I’m not buying your products until they’re more repair friendly.
That’s why I don’t buy laptops with soldered RAM.
In my opinion disadvantages of user-replaceable RAM far outweigh the advantages. The same goes for discrete GPUs. Apple moved away from this and I expect PC manufacturers to follow Apple’a move in the next decade or so, as they always do.
Here’s how I see the advantages of soldered RAM:
- better performance
- less risk of physical damage
- more energy efficient
- smaller
The risk of physical damage is so incredibly low already, and energy use of RAM is also incredibly low, so neither of those seem important.
So that leaves performance, which I honestly haven’t found good numbers for. If you have this, I’m very interested, but since RAM speed is rarely the bottleneck in a computer (unless you have specific workloads), I’m going to assume it to be a marginal improvement.
So really, I guess “smaller” is the best argument, and I honestly don’t care about another half centimeter of space, it’s really not an issue.
So that leaves performance, which I honestly haven’t found good numbers for. If you have this, I’m very interested, but since RAM speed is rarely the bottleneck in a computer (unless you have specific workloads), I’m going to assume it to be a marginal improvement.
This is where you’re mistaken. There is one thing that integrated RAM enables that makes a huge difference for performance: unified memory. GPUs code is almost always bandwidth limited, which why on a graphics card the RAM is soldered on and physically close to the GPU itself, because that is needed for the high bandwidth requirements of a GPU.
By having everything in one package, CPU and GPU can share the same memory, which means that you eliminate any overhead of copying data to/from VRAM for GPGPU tasks. But there’s more than that, unified memory doesn’t just apply to the CPU and GPU, but also other accelerators that are part of the SoC. What is becoming increasingly important is AI acceleration. UMA means the neural engine can access the same memory as the CPU and GPU, and also with zero overhead.
This is why user-replaceable RAM and discrete GPUs are going to die out. The overhead and latency of copying all that data back and forth over the relatively slow PCIe bus is just not worth it.
Do you have actual numbers to back that up?
The best I’ve found is benchmarks of Apple silicon vs Intel+dGPU, but that’s an apples to oranges comparison. And if I’m not mistaken, Apple made other changes like a larger bus to the memory chips, which again makes comparisons difficult.
I’ve heard about potential benefits, but without something tangible, I’m going to have to assume it’s not the main driver here. If the difference is significant, we’d see more servers and workstations running soldered RAM, but AFAIK that’s just not a thing.
The best I’ve found is benchmarks of Apple silicon vs Intel+dGPU, but that’s an apples to oranges comparison.
The thing with benchmarks is that they only show you the performance of the type of workload the benchmark is trying to emulate. That’s not very useful in this case. Current PC software is not build with this kind of architecture in mind so it was never designed to take advantage of it. In fact, it’s the exact opposite: since transferring data to/from VRAM is a huge bottleneck, software will be designed to avoid it as much as possible.
For example: a GPU is extremely good at performing an identical operation on lots of data in parallel. The GPU can perform such an operation much, much faster than the CPU. However, copying the data to VRAM and back may add so much additional time that it still takes less time to run it on the CPU, a developer may then choose to run it on the CPU instead even if the GPU was specifically designed to handle that kind of work. On a system with UMA you would absolutely run this on the GPU.
The same thing goes for something like AI accelerators. What PC software exists that takes advantage of such a thing?
A good example of what happens if you design software around this kind of architecture can be found here. This is a post by a developer who worked on Affinity Photo. When they designed this software they anticipated that hardware would move towards a unified memory architecture and designed their software based on that assumption.
When they finally got their hands on UMA hardware in the form of an M1 Max that laptop chip beat the crap out of a $6000 W6900X.
We’re starting to see software taking advantage of these things on macOS, but the PC world still has some catching up to do. The hardware isn’t there yet, and the software always lags behind the hardware.
I’ve heard about potential benefits, but without something tangible, I’m going to have to assume it’s not the main driver here. If the difference is significant, we’d see more servers and workstations running soldered RAM, but AFAIK that’s just not a thing.
It’s coming, but Apple is ahead of the game by several years. The problem is that in the PC world no one has a good answer to this yet.
Nvidia makes big, hot, power hungry discrete GPUs. They don’t have an x86 core and Windows on ARM is a joke at this point. I expect them to focus on the server-side with custom high-end AI processors and slowly move out of the desktop space.
AMD has the best papers for desktop. They have a decent x86 core and GPU, they already make APUs. Intel is trying to get into the GPU game but has some catching up to do.
Apple has been quietly working towards this for years. They have their UMA architecture in place, they are starting to put some serious effort into GPU performance and rumor has it that with M4 they will make some big steps in AI acceleration as well. The PC world is held back by a lot of legacy hard and software, but there will be a point where they will have to catch up or be left in the dust.
Andy Somerfield (@andysomerfield) on X
So, with the upcoming delivery of new #M1Pro and #M1Max hardware to customers next week, I thought I’d spend a couple of days talking about how @affinitybyserif Photo uses GPUs, how our benchmark works and what we should reasonably expect from this new hardware :)
I understand the scepticism, but without links of what you’ve found or which parts in particular you consider dubious claims (ram speed can be increased when soldered, higher speeds lead to better performance, etc) it comes across as “i don’t believe you, because i choose to not believe you”
LTT has made a comparison video on ram speeds: www.youtube.com/watch?v=b-WFetQjifc
Do you need proof that soldered ram can be made to run faster?
Yes, and the results from that video (i assume, I skimmed it, but have watched similar videos) is that the difference is negligible (like 1-10FPS) and you’re usually better off spending that money on something else.
I look at the benchmarks between the Intel MacBook Pro and the M1 MacBook Pro, and both use soldered RAM, yet the M1 gets so much better performance, even on non-GPU tasks (e.g. memory-heavy unit tests at work went from 3-5min to 45-50sec from latest Intel to M1). Docker build times saw a similar drop. But it’s hard for me to know what the difference is between memory vs CPU changes. I’d have to check, but I’m guessing there’s also the DDR4 to DDR5 switch, which increases memory channels.
The claim is that proximity to the CPU explains it, but I have trouble quantifying that. For me, a 1-10FPS drop isn’t enough to reduce repairability and expandability. Maybe it is for others though, but if that’s the difference, that’s a lot less than the claims they seem to make.
The video has a short section on productivity (i.e. rendering or compiling). That part is probably the most relevant for most people. Check the chapter view in YouTube to jump directly to it.
I think a 2x performance improvement is plausible when comparing non-soldered ram to the Apple silicon, which goes even further and has the memory on the die itself. If, of course, ram is the limiting factor.
short section on productivity
Looks about the same as the rest. Big gains for handbrake, pretty much nothing for anything else. And that makes sense, because handbrake will be doing lots of roundtrips to the GPU for encoding.
has the memory on the die itself
On the package, not the die. But perhaps that’s what you meant. On die would be closer to a massive cache like on the X3D AMD chips.
The performance improvement seems to be that Apple has a massive iGPU, not anything to do with RAM next to the CPU. So in CPU-only benchmarks, I’d expect the lion’s share of the difference to be CPU design and process node, not the memory.
Also, unified memory isn’t particularly new, APUs have supported it for years. It’s just not well utilized by devs because most users have dGPUs. So I think the main innovation here is Apple committing to it and providing tooling for devs to utilize the unified memory better, like console manufacturers have done.
So I guess that brings a few more questions:
- what performance improvements could we see if devs use unified memory in socketed LPDDR memory in laptops?
- how would that compare to Apple’s on-package RAM (I think it’s also LPDDR, so more apples to apples?)?
- how likely are AMD and Intel to push for massive APUs on laptops?
I guess we’re kind of seeing it with the gaming PC handhelds, like Steam Deck and Ayaneo etc al, so maybe that’ll become more mainstream.