I like Apple when they make pretty stuff. Especially small, shiny, and quiet.

The money's all in selling phones to teen girls now, and taking their mafia cut of app store sales.

They replaced it with Mac Neo. Did you notice the wonderful build quality, the accesible price and that everyone is buying it ? And it has USB: U from universal.

Would be a killer local AI setup...for $40k.

I bought a GPU maybe a decade ago for this, and it's not worth the hassle (for me at least), but a nice out-of-the box solution, I would pay for.

I don't find the external GPU houses for Mac Studio as appealing to use.

Believe t-shirts at WWDC were not enough.

Thus the workstation market joins OS X Server.

Mac OS is a horrible experience.

They made a shirt. It was fun.

The market for this use case is tiny

If the OpenAI domino falls, and I'd be happy to admit if I'm wrong, we're going to see a near catastrophic drop in prices for RAM and demand by the hyperscalers to well... scale. That massive drop will be completely and utterly OpenAI's fault for attempting to bite off more than it can chew. In order to shore up demand, we'll see NVidia and AMD start selling directly to consumers. We, developers, are consumers and drive demand at the enterprises we work for based on what keeps us both engaged and productive... the end result being: the ol' profit flywheel spinning.

Both NVidia and AMD are capable of building GPUs that absolutely wreck Apple's best. A huge reason for this is Apple needs unified memory to keep their money maker (laptops) profitable and performant; and while, it helps their profitability it also forces them into less performant solutions. If NVidia dropped a 128GB GPU with GDDR7 at $4k-- absolutely no one would be looking for a Mac for inference. My 5090 is unbelievably fast at inference even if it can't load gigantic models, and quite frankly the 6-bit quantized versions of Qwen 3.5 are fantastic, but if it could load larger open weight models I wouldn't even bother checking Apple's pricing page.

tldr; competition is as stiff as it is vicious-- Apple's "lead" in inference is only because NVidia and AMD are raking in cash selling to hyperscalers. If that cash cow goes tits up, there's no reason to assume NVidia and AMD won't definitively pull the the rug out from Apple.

Apple are winning a small battle for a market that they aren’t very good in. If you compare the performance of a 3090 and above vs any Apple hardware you would be insane to go with the Apple hardware.

When I hear someone say this it’s akin to hearing someone say Macs are good for gaming. It’s such a whiplash from what I know to be reality.

Or another jarring statement - Sam Altman saying Mario has an amazing story in that interview with Elon Musk. Mario has basically the minimum possible story to get you to move the analogue sticks. Few games have less story than Mario. Yet Sam called it amazing.

It’s a statement from someone who just doesn’t even understand the first thing about what they are talking about.

Sorry for the mini rant. I just keep hearing this apple thing over and over and it’s nonsense.

We should demand better of our computer-manufacturing overlords.

> It’s not like you can drop an RTX 5090 in there.

Why not? Oh, right, because Apple won't let you. Sad.

"Modular" does not mean that it's serviceable, repairable or upgradable. Apple's refusal to adopt basic M.2 spec is a pretty glaring example of that.

Anything that increases the necessary compute to fully utilize RAM bandwidth in optimal LLM serving weakens Apples advantage for that.

... that being said, and I did read https://www.home-assistant.io/blog/2025/09/11/ai-in-home-ass... or even have use a local modal for STT but IMHO it's not good, definitely not worth buying a $4k device for. It doesn't generate good rules, it gets some commands but it's quite basic. So I understand the concept but so far I haven't seen anything good enough to warrant such a purchase for such a very very specific niche. I can imagine literally a couple of persons interested in this but I'd bet they'd also have a GPU laying around that could be available for that too.

TL;DR: technically not impossible, I doubt it's a popular use case.

That being said if I were to be in such a situation, and if somehow the guarantees wouldn't be enough then I'd definitely expect to have the budget to build my own data center with GB300 or TPUs. I can't imagine that running it on a Mac Studio.

Dual 10G NIC cards, way cheaper than a comparable dongle 36 HDDs in JBOD, absolutely! 12 optical drives, sure!

But sure, more GPU cores will definitely fix it this time.

For all the faults of them leaning in hard on these things for stock market and personal gains, Nvidia still has some of the best quality products around. That is their saving grace.

They will not be the world most valuable company once the bubble pops, will probably never get back there again, but they will continue to be a decent enough business. I just want them going back to talking about graphics more than AI again, that will be nice.

As handwriting code is rapidly going out of fashion this year, it seems likely AI is coming for most of knowledge work next.

And who is to say that manual labor is safe for long?

I don't understand how this is a response to anything I said.

https://www.youtube.com/watch?v=x4_RsUxRjKU or something

“Apple has also confirmed to 9to5Mac that it has no plans to offer future Mac Pro hardware.”

2. Even with today's prices, storage is a relatively minor expense for professionals working at this level.

3. When material above 4K is used in a serious workflow, it tends to be at the acquisition phase only. In post production, raw files get transcoded to a proxy format (e.g. ProRes 422 Proxy) for editing.

4. In multi-user workflows, media is commonly accessed directly from shared network storage instead of duplicated onto individual machines.

5. Effects work is normally handled on a shot-by-shot basis. Even if they're working on local copies, we're talking mere minutes of raw material, if not seconds.

They're trying to make it very clear they're not speaking on behalf of Apple Inc, despite having worked (or working) there.

Big companies like to give employees some minimal "media training", which mostly amounts to "do not speak for the company, do not say anything that might even slightly sound like you're speaking for the company".

  > > Opinions are my own obvs.

  > Whose else would they be?

> Whose else would they be?

takes a look at the user profile

Oh, they are a journalist/writer for a big name outfit

Remember when a $1 billion valuation used to be a big thing? That is nothing compared with nowadays.

For money, probably.

Apple is presumably leaving a lot of money on the table by not trying to sell Apple Silicon for AI inference and training. They're the only ones who can attach reasonably large GPUs (M3 Ultra) to very large amounts of cheaper memory (512GB SO-DIMM per GPU). Apple could e.g. sell server SKUs of Mac Studios, heck they can sell M3 Ultra chips on PCIe cards. And they could further develop Apple Silicon in that direction. Presumably they would be seen as a very legit competitor to Nvidia that way, perhaps moreso than Intel and AMD. I'd assume that in the current climate this would be extremely lucrative.

Now, actually doing this would disrupt Apple's own supply chain as well as force it to spend significant internal resources and cultural change for this kind of product line. There's a good argument to be made it would disproportionally negatively affect its Mac business, so this would be a very risky move.

But given that AI hardware is likely much higher margin than the Mac business an argument could probably (sadly) be made that it'd be lucrative for them to try it. I personally don't think Apple is inclined to take this kind of risk to jeopardize the Mac, but I'm sure some people at Apple have considered this.

It would be even cooler if that box was also housing my computer and powered by the same power supply.

And then the PCIe lanes could just run to the CPU/SoC instead of having to be wrapped in Thunderbolt.

Same reason as a) GDDR on dGPUs (I think I read somewhere that GDDR is very much like regular DDR, just with much tighter paths and thus soldered in) and b) Framework Desktop (performance would reportedly halve if RAM were not soldered)

SSD reasons I seem to recall are architectural for security: some parts (controller?) that usually sit on a NVMe SSD are embedded in the SoC next to (or inside?) the secure enclave processor or whatever the equivalent of the T2 thing is in Mx chips, so what you'd swap would be a bank of raw storage chips which don't match the controller.

The soldering does serve a purpose though, the shorter traces allow for better signal integrity at higher speeds. This isn't something special about what Apple is doing though, Intel and AMD are doing the exact same thing with the exact same LPDDR5 chips on their respective APUs.

HBM is still almost purely reserved for datacentre GPUs.

No. There is a reason for it but no, it's just soldered on the same carrier board as the APU, in order to be really close to it. Apple could have used a form factor like CAMM2 and it would have worked the same, be it at slightly higher cost. The reason is simply to kill upgrade options and cut manufacturing costs - same as for any other soldered ram.

Give every iPhone family a in house Siri that will deal with canceling services and pursuing refunds.

Your customer screw up results in your site getting an agent drive DDOS on its CS department till you give in.

Siri: "Hey User, here's your daily update, I see you haven't been to the gym, would you like me to harass their customer service department till they let you out of their onerous contract?"

Unless the one it comes with isn't as fast as the one you want, or they didn't integrate one at all, or you need more than one.

> Across all desktop PCs, the most common number of slots filled is one (a single GPU), and the average is surely less than one (systems using zero slots and relying on integrated graphics must greatly outnumber systems using more than one slot).

There is an advantage in having an empty slot because then you can put something in it.

Your SSD gets full, do you want to buy one which is twice as big and then pay twice as much and screw around transferring everything, or do you want to just add a second one? But then you need an empty slot.

You bought a machine with an iGPU and the CPU is fine but the iGPU isn't cutting it anymore. Easy to add a discrete GPU if you have somewhere to put it.

The time has come to replace your machine. Now you have to transfer your 10TB of junk once. You don't need 100Gbps ethernet 99% of the time, but using the builtin gigabit ethernet for this is more than 24 hours of waiting. A pair of 100Gbps cards cuts that >24 hours down to ~15 minutes. If the old and new machines have an empty slot.

I have a Lian Li anniversary edition snail case and I don’t think any moveable desk could hold it.

There is basically no lock-in, you don't even "move" your image, your data is basically some "context" or a history of prompts which probably fits in a floppy disk (not even being sarcastic) so if you know the basic about containerization (Docker, podman, etc) which most likely the cloud provider even takes care of, then it takes literally minutes to switch from one to another. It's really not more complex that setting up a PHP server, the only difference is the hardware you run on and that's basically a dropdown button on a Web interface (if you don't want to have scripts for that too) then selecting the right image (basically NVIDIA support).

Consequently even if that were to happen (which I have NEVER seen! at worst it's like 15% increase after years) then it would actually not matter to you. It's also very unlikely to happen based of the investment poured into the "industry". Basically everybody is trying to get "you" as a customer to rely on their stack.

... but OK, let's imagine that's not appealing to you, have you not done the comparison of what a Mac Studio (or whatever hardware) could actually buy otherwise?

Even if Apple had an amazing GPU for AI it wouldn’t matter hugely - local inference hasn’t taken off yet and cloud inference and training all uses servers where Apple has no market share and wasn’t going to get it since people had already built all the stacks around CUDA before Apple could even have awoken to that.

Sound recording engineers and video editors will not disappear after the AI bubble bursts, and Apple is wise to keep that market. Bursting the AI bubble will not make AI disappear, it will just end the crazy cashflows we are seeing now. And in that regard, with the capabilities of their hardware, Apple is in a pretty good spot I think.

If you want to get usable speeds from very large models that haven't been quantitized to death on local machines, RDMA over Thunderbolt enables that use case.

Consumer PC GPUs don't have enough RAM, enterprise GPUs that can handle the load very well are obscenely expensive, Strix Halo tops out at 128 Gigs of RAM and is limited on Thunderbolt ports.

With USB3 you have 94 i/o…

For years pci has not been mandatory for audio. UAD, Apogee, RME and other high end brands will push you to them. Or even only provide them as usb device… even Thunderbolt is not needed here.

And that’s been the case for a while! My Fireface UC from 15 years ago can deal with 16 channels at 96khz at 256 sample. On PC and Mac.

Smaller models have gotten much more powerful the last 2 years. Qwen 3.5 is one example of this. The cost/compute requirements of running the same level intelligence is going down

I always hoped we’d get a consumer version of what they have internally - 10 or 20 or more Apple Silicon chips for 1000 cores or so.

You would use multiple *90-series GPUs, throttled down in terms of power. Depending on the GPU, the sweet spot is between 225-350W, where for LLM workloads you only lose 5-10% of performance for a ~50% drop in power consumption.

Combined with a workstation (Xeon/Epyc) CPU with lots of PCIe, you can support 6-7 such GPUs (or more, depending on available power). This will blow away the fastest Mac studio, at a comparable performance per watt.

Again, a lot of this has changed, since GPUs and memory are so much more expensive now.

Macs are great for a simpler all in one box with high memory bandwidth and middling-to-decent GPU performance, but they are (or were) absolutely not "untouchable."

Apple could have taken a chunk of the enterprise market now with that AI craze if they had made an upgradable and expandable server edition based on their silicon. But no, everything has to be bolt down and restricted.

And power consumption !

The performance per watt of Apple is unmatched.

Thunderbolt is also too slow for higher-end networks. A single port is already insufficient for 100-gigabit speeds.

That's only the case for CPU/MB/RAM, because the interfaces are tightly coupled (you want to upgrade your CPU, but the new one uses an AM5 socket so you need to upgrade the motherboard, which only works with DDR5 so you need to upgrade your RAM). For other parts, a "Ship of Theseus" approach is often worth it: you don't need to replace your 2TB NVMe M.2 storage just because you wanted a faster CPU, you can keep the same GPU since it's all PCIe, and the SATA DVD drive you've carried over since the early 2000s still works the same.

Your point kind of disproves your point.

I/O expansion

Networking

There are none currently on eBay.co.uk, so I'm going to try there. I'll also try some of the reddit UK-specific groups.

As far as not being scammed - it's a really high value one-off sale, so it'll either be local pickup (and cash / bank-transfer at the time, which happens in seconds in the UK) or escrow.com (for non-eBay) with the buyer paying all the fees etc.

I'd prefer local pickup because then I have the money, the buyer can see it working, verify everything to their satisfaction etc. etc.

> Wish you a speedy recovery for your back!

Thank you :) It is a little better today. Sitting down is now tolerable for short periods... :)

(I speak as an experienced third-party seller on Amazon/Walmart/eBay.)

Also: All things being equal, the lightening connector was technically superior to USB-C and arrived much earlier.. so it's somewhat on the same path.

USB-C succeeded due to a confluence of;

A) Being a standard people can get behind. (lightning was, of course, much more awkwardly licensed)

B) Lightning never got a sufficient uplift from USB-2.0 performance.

C) The EU eventually killed lightening through regulation.

It was, however, smaller, more durable and (as mentioned) earlier.

I'm totally not against our new USB-C everywhere situation w.r.t. phones, but if anything it reinforces the point: The technically superior thing being too proprietary caused its death (despite being early).

For my own use, I'm just looking at absolute price (and convenience).

I haven't explored open weights models, so I have no idea which I'd want. It would be great to get a "frontier" model like Minimax-M2.5, but at $10/hr, it's not worth it - let alone $40/hr for GLM-5. I'd have to explore use cases for cheaper models. Likely for things related to reading emails, I can get by with a much cheaper model.

If I set one of these up, how easily is it for me to launch one of these (on the command line on my home PC) and then shut it down. Right now, when I write any app (or use OpenCode), it's frictionless. My worry is that either turning it on will be a hassle, and even worse, I'll forget to turn it off and suddenly get a big pointless bill.

If there are any guides out there on how people manage all this, it would be much appreciated.

TIL:

* https://developer.apple.com/documentation/technotes/tn3205-l...

Or maybe I forgot:

* https://news.ycombinator.com/item?id=46248644

I suppose that splitting an LLM workload is pretty sensitive to that.

the bottleneck in lots of database workloads is memory bandwidth. for example, hash join performance with a build side table that doesn't fit in L2 cache. if you analyze this workload with perf, assuming you have a well written hash join implementation, you will see something like 0.1 instructions per cycle, and the memory bandwidth will be completely maxed out.

similarly, while there have been some attempts at GPU accelerated databases, they have mostly failed exactly because the cost of moving data from the CPU to the GPU is too high to be worth it.

i wish aws and the other cloud providers would offer arm servers with apple m-series levels of memory bandwidth per core, it would be a game changer for analytical databases. i also wish they would offer local NVMe drives with reasonable bandwidth - the current offerings are terrible (https://databasearchitects.blogspot.com/2024/02/ssds-have-be...)

Apple needs to solder it because they are attaching it directly to the SOC to minimize lead length and that is part of how they are able to get that bandwidth.

Isn't that also because that's world we have optimized workloads for?

If the common hardware had unified memory, software would have exploited that I imagine. Hardware and software is in a co-evolutionary loop.

I think you can do better than the proverbial Apples and Oranges comparison.

In terms of total system, "box on desk", Apple is likely to remain the performance per watt leader compared to random PC workstations with whatever GPUs you put inside.

There are also SO-DIMM options.

Come on mate ... I think you and I both know I was talking about complete system here, not discrete components.

I'm pretty sure your total package (Dell Pro Max + GB10) will pull more from the wall.

Yes, that's my point.

It's the new thing. All three of the DRAM manufacturers intend to produce it:

https://www.digitimes.com/news/a20240916PD207/samsung-lpcamm...

And it's called "CAMM2" because it's not even the first version. Apple could have been working with the other OEMs on this since 2022 and been among the first to adopt it instead of the last:

https://www.techpowerup.com/294240/dells-ddr5-camm-appears-i...

Back then Maya seemed like this unobtainable, magical machine for producing impossible imagery. When I finally got my hands on it, I was so disappointed to realize I still needed skills to make it do the cool things. I was ~16 and pretty clueless. I just knew Maya was used for the crazy stuff I was seeing in cut scenes from games or special effects in movies.

The premise of the connector is that it attaches to the board in a similar way as soldering the chips (the LPCAMM connection interface is directly on the back of the chips) but uses compression instead of solder to make the electrical connection, so the traces are basically the same length but the modules can be replaced without soldering. There is no reason you couldn't use two modules to get a 256-bit memory bus. It sounds like AMD designed Strix Halo to assume soldered memory and then when Framework asked if it could use CAMM2 with no modifications to the chip, the answer was yes but not at the full speed of the CAMM2 spec.

CAMM2 supports LPDDR5X-9600, which is the same speed Apple uses in the newest machines:

https://www.micron.com/content/dam/micron/global/public/docu...

The Max chips have a 512-bit memory bus. That's the one where the comment you linked suggests putting one module on each side of the chip as being fine, and there is no M4 Ultra or M5 Ultra so they could be using LPCAMM2 for their entire current lineup. The M3 Ultra had a 1024-bit memory bus, which is a little nuts, but it's also a desktop-only chip and then you don't have to be fighting with the trace lengths for LPDDR5 because you could just use ordinary DDR5 RDIMMs.

All the information I can find suggests that CAMM2 will have higher parasitic resistance and capacitance than a soldered connection. Do you have a source for this claim?

The issue isn't just reaching a certain speed, but doing it at the same power consumption.

>The modules take up approximately the same amount of space on the board as the chips themselves.

They do take up more space, as anyone can easily check. Modern laptop motherboards can be very small, so this is significant.

>DDR5 and LPDDR5 are nearly identical [...]

What I mean is that Apple isn't going to want to invest any resources in adding the option of external RAM just for the relatively tiny desktop market. It's not that it's technically difficult; it just doesn't make sense from a logistical point of view.

The cheapest Apple desktop with 128GB of memory shows up as costing $3499 for me, which isn't very "enthusiast-compatible", it's about 3x the minimum salary in my country!

For instance, if you cluster Mac Studios over TB5 with RDMA, the performance can be pretty stellar. It may not be more cost effective than renting compute for the same tasks, but if you've got (up to) four M3 Ultras with a ton of RAM, you'll be hard pressed to find something similar.

That's still not more ideal than having native alternatives like OCuLink or something that can be networked like QSFP, but it's a fair way to highlight the current design's strengths.

https://www.tomshardware.com/pc-components/gpus/oculink-outp...

Why? Enthusiasts are by definition people for whom value for money is not the main driver but top performance and cutting edge novelty at any cost. Affording enthusiast computer hardware is not a human right same how affording a Lamborghini or McMansion isn't.

But you don't need to buy a Lamborghini to do your grocery shopping or drive your kids to school, same how you don't need an Nvidia 5090 or MacBook Pro Max to do your taxes or do your school work.

So the definition is fine as it is. It's hardware for people with very deep pockets, often called whales.

Enthusiast in this contest more or less means you are excited enough about something to get a level above what normal people should get and just below professional pricing. An enthusiast camera body can be 2000 euros.

I would say an enthusiast computer is 2-4k.

It really depends what you meant with minimum salary (yearly?) because paying 3 months of salary for a computer like that isn't far fetched. You're not using this to generate recipes for cookies. An enthusiast level car is expensive as well.

But you still need the working set of frequently used experts to actually fit in RAM, or at least stay cached. Expert routing happens per token, per layer. If those weights aren’t resident, you’re effectively pulling them from disk on the critical path of generation — over and over again.

That’s not “just slower,” that’s order of magnitude slower. You’ll end up with constant page faults and page cache churn. And if swap is on the same device as the model, you’re now competing for bandwidth on top of that.

IMO the main benefit of mmap is ability to reclaim cold pages during high memory-pressure events when model isn't active.

Regardless - there's a difference between training and inference. And pytorch doesn't magically make 5 gpus behave like 1 gpu.

In the most literal meaning, absolutely, "Enthusiast" just means a person who likes something, is excited about something.

When it comes to market and products though, typically you'll see the word "Enthusiast" as mid-tier - something like: Consumer --> Enthusiast --> Professional (may have words like "Prosumer" in there as well etc:)

In that context, which is typically the one people will use when discussing product pricing and placement, "Enthusiast" is somebody who yes enjoys something, but does it sufficiently to be discerning and capable of purchasing mid-tier or above hardware.

So while a consumer photographer, may use their phone or compact or all-in-one camera, enthusiast photographer will probably spend $3000 - $5000 in camera gear. Equivalently, there are myriad gamers out there (on phones, consoles, Geforce Now, whatever:), an enthusiast gamer is assumed to have a dedicated gaming computer, probably a tower, with a dedicated video card, likely say a 5070ti or above, probably 32GB+ RAM, couple of SSDs which are not entry level, etc.

Again, this is not to say a person with limited budget is "not a real enthusiast", no gatekeeping is intended here; simply, if it may help, what the word means when it comes to market segmentation and product pricing :)

It's out of reach for lots of people, even in developed countries. But it's easily within reach for loads of people that care more about computing than other stuff.