on a phone? I mean i suppose you could do that, but VNC is not a very slick remote access tool for anything other than, well, remote access. The latency and speed over WIFI would be a significant problem, i suppose you could stream from your phone to your TV, but again, most TVs that exist today are smart TVs so literally a non issue.

my example here was using a computer rather than a phone, to show that even desktop computing tasks, don’t really use all that much ram.

you could be rendering, simulating, running virtual machines

On a phone? I guess you could, although 4gb is probably enough for any video game that any amount of people use.

on a phone? Why the fuck would anyone be running virtual machines on a phone?

Web browsers also eat ram.

Right? It’s nice to have the occasional reverse tri head metric upside down weird random bit when you need it.

I’ve taken apart so so so many things… sometimes for the right reasons and sometimes for the wrong reasons…my ZuneHD still works. I’ll never ever try to open a Surface product.

I don’t know if I’d recommend it, but if you are absolutely set on having the Thinkpad nipple – I don’t use it, even if I really want the Thinkpad trackpad – the factory that made the original IBM Model M keyboards is still in business somewhere in Kentucky. IIRC the employees bought it or something when IBM stopped making the things. They offer a nipple keyboard, goes by the name of “Endura Pro”. checks Unicomp. That’s the remnants in the US of the IBM business; the Chinese Lenovo purchased the laptops and also do the Trackpoint.

I got one like twenty years back, and while the actual buckling-spring keyswitches on the keyboard are pretty much immune to time, I wore out the switches on the mouse buttons, so I don’t know if I can give a buy recommendation for the mouse-enabled version (though maybe they improved the switches there). But if you really, really like it, that might be worthwhile for you. Last I looked they were still making them.

checks

They’ve got a message up saying that a supplier of a component used in that keyboard went under due to COVID so they suspended production. I don’t know what the status is on that.

https://www.pckeyboard.com/mm5/merchant.mvc?Screen=CTGY&Category_Code=EnduraPro

NOTICE CONCERNING AVAILABILITY – Unfortunately, we have had to temporarily suspend the sale of the Endura Pro keyboards due to another supply chain shortage. The supplier of one of the flex harnesses had to close their doors during the pandemic. We’ve begun the task of sourcing a new supplier but do not have a definite time frame for when these keyboards will be available again. For our customers with orders already placed, we have enough stock to complete all on order.

Keep in mind that this is a very large, heavy keyboard that you could brain someone with; if you’re going to haul it around with a laptop, it’s going to be larger and heavier than the laptop. Mentioning it mostly since I figure that you might use it at some location where you could leave the keyboard.

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.

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: https://www.youtube.com/watch?v=b-WFetQjifc

Do you need proof that soldered ram can be made to run faster?

“unified memory” is an Apple marketing term for what everyone’s been doing for well over a decade.

Wrong. Unified memory (UMA) is not an Apple marketing term, it’s a description of a computer architecture that has been in use since at least the 1970’s. For example, game consoles have always used UMA.

Every single integrated GPU in existence shares memory between the CPU and GPU; that’s how they work.

Again, wrong.

While iGPUs have existed for PCs for a long time, they did not use a unified memory architecture. What they did was reserve a portion of the system RAM for the GPU. For example on a PC with 512MB RAM and an iGPU, 64MB may have been reserved for the GPU. The CPU then had access to 512-64 = 448MB. While they shared the same physical memory chips, they both had a separate address space. If you wanted to make a texture available to the GPU, it still had to be copied to the special reserved RAM space for the GPU and the CPU could not access that directly.

With unified memory, both CPU and GPU share the same address space. Both can access the entire memory. No RAM is reserved purely for the GPU. If you want to make something available to the GPU, nothing needs to be copied, you just need to point to where it is in RAM. Likewise, anything done by the GPU is immediately accessible by the CPU.

Since there is one memory pool for both, you can use RAM more efficiently. If you have a discrete GPU with 16GB VRAM, and your app only needs 8GB VRAM, that other memory just sits there being useless. Alternatively, if your app needs 24GB VRAM, you can’t run it because your GPU only has 16B, even if you have lots of system RAM available.

With UMA you can use all the RAM you have for whatever you need it for. On an M2 Ultra with 192GB RAM you can use almost all of that for the GPU (minus a little bit that’s used for the OS and any running apps). Even on a tricked out PC with a 4090 you can’t run anything that needs more than 24GB VRAM. Want to run something where the GPU needs 180MB of memory? No problem on an M1 Ultra.

It has nothing to do with soldering the RAM.

It has everything to do with soldering the RAM. One of the reason iGPUs sucked, other than not using UMA, is that GPUs performance is almost limited by memory bandwidth. Compared to VRAM, standard system RAM has much, much less bandwidth causing iGPUs to be slow.

A high-bandwidth memory bus, like a GPU needs, has a lot of connections and runs at high speeds. The only way to do this reliably is to physically place the RAM very close to the actual GPU. Why do you think GPUs do not have user-upgradable RAM?

Soldering the RAM makes it possible to integrate a CPU and an non-sucking GPU. Go look at the inside of a PS5 or XSX and you’ll see the same thing: an APU with the RAM chips soldered to the board very close to it.

This again has little to do with being socketed though: LPCAMM supports up to 9.6GT/s, considerably faster than what ships with the latest macs.

LPCAMM is a very recent innovation. Engineering samples weren’t available until late last year and the first products will only hit the market later this year. Maybe this will allow for Macs with user-upgradable RAM in the future.

The only way discrete GPUs can possibly be outcompeted is if DDR starts competing with GDDR and/or HBM in terms of bandwidth

What use is high bandwidth memory if it’s a discrete memory pool with only a super slow PCIe bus to access it?

Discrete VRAM is only really useful for gaming, where you can upload all the assets to VRAM in advance and data practically only flows from CPU to GPU and very little in the opposite direction. Games don’t matter to the majority of users. GPGPU is much more interesting to the general public.

Read my other replies to this comment. There’s no GPU. It’s an SoC.

Maybe you’re not familiar with the apps I’m referring to. Final Cut Pro and Logic Pro are professional video and audio workstations.

If I tried to master an export from Adobe Premiere Pro in Protools on PC I’d need 32GB of RAM to to prevent stutter. I only use ~12GB of 16GB doing the same on Apple Silicon.

8GB of UM is not for someone running two pro apps at once. It’s for grandma to use for online banking and check her email and Facebook.

Negative.

https://www.apple.com/newsroom/2023/10/apple-unveils-m3-m3-pro-and-m3-max-the-most-advanced-chips-for-a-personal-computer/

I’m talking about practical use performance. I understand your world, you don’t understand mine. I’ve been taking apart and upgrading PCs since the 286. I understand benchmarks. What you don’t understand, is how MacOS uses the SoC in a way where benchmarks =/= real-world performance. I’ve used pro apps on powerful PCs and powerful Macs, and I’m speaking from experience. We can agree to disagree.