MacBook Pro with M5 Pro now comes standard with 1TB of storage, while MacBook Pro with M5 Max now comes standard with 2TB. And the 14-inch MacBook Pro with M5 now comes standard with 1TB of storage.Also, the mix of cores have changed drastically.
- 6 "Super cores"
- 12 "Performance cores"
I'm guessing these are just renamed performance and efficiency cores from previous generations.
This is a massive change from the M4 Max:
- 12 performance cores
- 4 efficiency cores
This seems like a downgrade (in core config but may not be in actual MT) assuming super = performance and performance = efficiency cores.
> The industry-leading super core was first introduced as performance cores in M5, which also adopts the super core name for all M5-based products
But new "performance" is claimed to be new design (= not just overclocked efficiency core from M5?):
> M5 Pro and M5 Max also introduce an all-new performance core that is optimized to deliver greater power-efficient, multithreaded performance for pro workloads.
quotes from https://www.apple.com/newsroom/2026/03/apple-debuts-m5-pro-a...
The base M5 has super/efficiency cores.
The Pro and Max have super/performance cores.
I think this is a new design, with Apple having three tiers of cores now, similar to what Qualcomm has been doing for a while.
I think how it breaks down is:
- "Super" are the old "P" cores, and the top tier cores now
- "Performance" cores are a new tier and seen for the first time here, slotting between "old" P and E in performance
- "Efficiency" / "E" are still going to be around; but maybe not in desktop/Pro/Max anymore.
For example, 6 super, 8 performance, and 4 efficiency.
I believe they lower the clock speed, limit how much work is done in parallel on each core, and limit how aggressive the speculative execution is so less work is wasted.
The M5 performance cores can be scaled down to match efficiency cores in performance and power usage.
Are they doubling down on local LLMs then?
I still think Apple has a huge opportunity in privacy first LLMs but so far I'm not seeing much execution. Wondering if that will change with the overhaul of Siri this spring.
Remains to be seen how capable it actually is. But they're certainly trying to sell the privacy aspect.
It's the best. We all turned it off. 100% privacy.
Now extrapolating in line with how Sun servers around year 2000 cost a fortune and can be emulated by a 5$ VPS today, Apple is seeing that they can maybe grab the local LLM workloads if they act now with their integrated chip development.
But to grab that, they need developers to rely less on CUDA via Python or have other proper hardware support for those environments, and that won't happen without the hardware being there first and the machines being able to be built with enough memory (refreshing to see Apple support 128gb even if it'll probably bleed you dry).
https://survey.stackoverflow.co/2025/technology/#1-computer-...
The US 1s? Is that why we have Deepseek and then other non-US open source LLMs catching up rapidly?
World view please. The developer community is not US only.
It wouldn’t surprise me if the deepseek people were primarily using Mac’s. Maybe Alibaba might be using PCs? I’m not sure.
Basically, too many choices to "focus on" makes non a winner except the incumbent.
Are they doubling down on local LLMs then?
Apple's stated AI strategy is local where it can and cloud where it needs. So "doubling down"? Probably not. But it fits in their strategy.
I don't mind it, I open Apple stock. But I'm def not buying into their rebranding of integrated GPU under the guise of Unified Memory.
And while it is stupid slow, you can run models of hard drive or swap space. You wouldn’t do it normally, but it can be done to check an answer in one model versus another.
Aren't the OpenClaw enjoyers buying Mac Minis because it's the cheapest thing which runs macOS, the only platform which can programmatically interface with iMessage and other Apple ecosystem stuff? It has nothing to do with the hardware really.
Still, buying a brand new Mac Mini for that purpose seems kind of pointless when a used M1 model would achieve the same thing.
Yeah, because Mac upgrade prices were already sky high before the shortage. 32GB of DDR5-6000 for a PC rocketed from $100 to $500, while the cost of adding 16GB to a Mac was and still is $400.
That's likely only part of the reason. Mac Mini is now "cheap" because everyone exploded in price. RAM and SSD etc have all gone up massively. Not the mention Mac mini is easy out of the box experience.
I considered the mac mini at the time, but the mac mini only makes sense if you need the local processing power or the apple ecosystem integration. It's certainly not cheaper if you just need a small box to make API calls and do minimal local processing.
Do you really need Openclaw now? And not claude code + zapier or Claude code + cron?
That's the point. If you have worse CPU and GPU Windows will be sluggish (it's bloated).
If you just need "a small box to make API calls and do minimal local processing" you an also just buy a RPI for a fraction of the price of the GMKtec G10.
All 3 serve a different purpose; just because you can buy a slower machine for less doesn't mean the price:performance of the M1 Mac Mini changes.
For the same price in API calls, you could fund AI driven development across a small team for quite a long while.
Whether that remains the case once those models are no longer subsidized, TBD. But as of today the comparison isn't even close.
I just don't get why they're dropping the ball so much on this.
They aren’t dropping the ball, they are being smart and prudent.
Honestly, I think that's the move for apple. They do not seem to have any interest in creating a frontier lab/model -- why would they give the capex and how far behind they are.
But open source models (Kimi, Deepseek, Qwen) are getting better and better, and apple makes excellent hardware for local LLMs. How appealing would it be to have your own LLM that knows all your secrets and doesnt serve you ads/slop, versus OpenAI and SCam Altman having all your secrets? I would seriously consider it even if the performance was not quite there. And no need for subscription + cli tool.
I think apple is in the best position to have native AI, versus the competition which end up being edge nodes for the big 4 frontier labs.
I think I'll pass on upgrading.
So yes, the LLM should be inferencing on your prompt, but it should also be inferencing on 25,000 other things … in parallel.
Those are the compute needs.
We just need compute everywhere as fast as possible.
I assume they have a moderate bet on on-device SLMs in addition to other ML models, but not much planned for LLMs, which at that scale, might be good as generalists but very poor at guaranteeing success for each specific minute tasks you want done.
In short: 8gb to store tens of very small and fast purpose-specific models is much better than a single 8gb LLM trying to do everything.
"AI" (LLMs) may or may not have a bubble-pop moment, but until it does Apple get to ride it on these press releases and claims. But if the big-pop occurs, then Apple winds up with really fantastic hardware that just happens to be good at AI workloads (as well as general computing).
For example, image classification (e.g. face recognition/photo tagging), ASR+vocoders, image enhancement, OCR, et al, were popular before the current boom, and will likely remain popular after. Even if LLM usage dries up/falls out of vogue, this hardware still offers a significant user benefit.
So as most people in or adjacent to the AI space know, NVidia gatekeeps their best GPUs with the most memory by making them eye-wateringly expensive. It's a form of market segmentation. So consumer GPUs top out at 16GB (5090 currently) while the best AI GPUs (H200?) is 141GB (I just had to search)? I think the previou sgen was 80GB.
But these GPUs are north of $30k.
Now the Mac Studio tops out currently at 512GB os SHARED memory. That means you can potentially run a much larger model locally without distributing it across machines. Currently that retails at $9500 but that's relatively cheap, in comparison.
But, as it stands now, the best Apple chips have significantly lower memory bandwidth than NVidia GPUs and that really impacts tokens/second.
So I've been waiting to see if Apple will realize this and address it in the next generation of Mac Studios (and, to a lesser extend, Macbook Pros). The H200 seems to be 4.8TB/s. IIRC the 5090 is ~1.8TB/s. The best Apple is (IIRC) 819GB/s on the M3 Ultra.
Apple could really make a dent in NVidia's monopoly here if they address some of these technical limitations.
So I just checked the memory bandwidth of these new chips and it seems like the M5 is 153GB/s, M5 Pro is ~300 and M5 Max is ~600. I was hoping for higher. This isn't a big jump from the M4 generation. I suspect the new Studios will probably barely break 1TB/s. I had been hoping for higher.
5090 has 32GB, and the 4090 and 3090 both have 24GB.
I also haven’t seen any improvements in the frontier models in years, and I’m anxiously awaiting local models to catch up.
Apple is in the hardware business.
They want you to buy their hardware.
People using Cloud for compute is essentially competitive to their core business.
This correlation of Apple and privacy needs to rest. They have consistently proven to be otherwise - despite heavily marketing themselves as "privacy-first"
https://www.theguardian.com/technology/2019/jul/26/apple-con...
Do think it'll be common to see pros purchasing expensive PCs approaching £25k or more if they could run SoTA multi-modal LLMs faster & locally.
Neural Accelerators (aka NAX) accelerates matmults with tile sizes >= 32. From a very high level perspective, LLM inference has two phases: (chunked) prefill and decode. The former is matmults (GEMM) and the latter is matrix vector mults (GEMV). Neural Accelerators make the former (prefill) faster and have no impact on the latter.
Here in Europe, including 21% VAT, that's €6.124,00 ($7.094,35 equivalent).
Because of pricing strategies and such, the 128GiB version comes with a 2TiB SSD at minimum, and also requires the M5 Max (not Pro) at its highest configuration.
Not sure if this is new, but it should be noted that these laptops don't come with a charger any more.
70W USB-C Power Adapter (included with M5 Pro with 16-core GPU)
96W USB-C Power Adapter (included with M5 Pro with 20-core GPU, configurable with M5 Pro with 16-core GPU)
USB-C to MagSafe 3 Cable (2 m)Literally unusable
I already left the beta train on my iPhone because I had too many issues getting my grocery apps to allow me to place orders without going to my laptop and doing it in a web browser.
It's so bad I switched back to Chrome. I had thought Chrome had a major battery life penalty compared to Safari on Macs, but I checked more up-to-date info and apparently that's outdated.
I use my laptop for development. I don't actually use most of the built in applications. My browser is Firefox, I use codex, vs code, intellij, iterm2, etc. Most of that works just fine just as it did on previous versions of the OS. I actually on purpose keep my tool chains portable as I like to have the option to switch back to Linux when I want to. I've done that a few times. I come back for the hardware, not the OS.
In my experience, if you don't like Apple's OS changes that is unfortunate but they don't seem to generally respond to a lot of the criticism. Your choices are to get further and further out of date, switch to something else, or just swallow your pride. Been there done that. Windows is a "Hell No" for me at this point. I'll take the UX, with all the pastel colors that came and went and all the other crap that got unleashed on macs over the last ten years. Definitely a case of the grass not being greener on Windows. Even with the tele tubby default desktop in XP back in the day.
I can deal with Linux (and use that on and off on one of my laptops). However, that just doesn't run that well on mac hardware. And any other hardware seems like a big downgrade to me. Both Windows and Linux are arguably a lot worse in terms of UX (or lack thereof). Linux you can tweak. And you kind of have to. But it just never adds up to consistent and delightful. Windows, well, at this point liking that is probably a form of Stockholm Syndrome. If that doesn't bother you, good for you.
So, Mac OS it is for me as everything else is worse. I've in the past deferred updates to new versions of Mac OS as well. Generally you can do that for a while but eventually it becomes annoying when things like homebrew and other development toys start assuming you run something more recent. And of course for security reasons you might just not drag your feet too long. Just my personal, pragmatic take.
But I think this predates Tahoe.
They also probably had RAM contracts in place far enough in advance to avoid the worst of the price spikes.
And another rumor said these are going to be updated again this fall but I’m not sure about that. With OLED screens and M6 (supposedly).
I think at this point Apple will just release new versions of laptops whenever new CPU revisions and yields allow. M5 Pro wasn't ready for October so delayed until now.
Interestingly, 36-128GB models are showing as “currently unavailable” on the store page, and you can’t even place an order for them right now? But for anyone curious, it’s quoting $5099 for the 128GB RAM 14” MacBook Pro model.
Interesting that this hasn't budged since the memory shortages appeared.
Apple has had enough war chests with the ability of buying the entirety of TSMC's new capacity years in advance in the past.
If I were to guess, Apple locked in their entire BOM and production capacity two years ago. That's something even the large players cannot replicate because they run cash-lean and have too many different SKUs, and the small players (Framework, System76, even Steam) are entirely left to the forces of the markets.
I'd love to have customers like Apple. Bumps $200: "it didn't change!!!"
And no power adapter included.
To be fair, ever since the advent of high power USB-C PD that really, really is not needed any more, way too many power bricks are effectively e-waste.
People already have USB-C power bricks and docks everywhere and unlike pre-USB-C generations, you can use them not just across different generations of hardware, but across vendors as well.
Now it starts at $1699, a $100 bump but comes with a 1TB SSD. Previously it would have cost $1799 for the 1T SSD, so it's a $100 bump on base price but you are also getting 1TB SSD for $100 less than before.
I wonder if that would happen regardless of RAM, e.g. for tariffs etc.
Try making a good product that people love?
No change from the previous models then, 16GB->32GB was already $400. They're cutting into their previously enormous margins to keep the prices stable, rather than hiking the prices to maintain their margins.
Isn't this it?
My M3 Pro from a few years ago for the same price had 18GB.
You can run open source models like Kimi K or Qwen locally. Apple recently updated Xcode 26.3 to support local models.
I have not once felt the need to upgrade in years, and that’s with doing pretty demanding 3D and LLM work.
Even if a new device is a small upgrade from last year's model, it can be a giant upgrade for other people.
and that’s with doing pretty demanding 3D and LLM work.
M5 Max maxes out at 128GB, so that will have to wait for the eventual M5 Ultra anyways.
The high memory Macs have been great for being able to run LLMs, but the prompt processing has always been on the slow side. The new AI acceleration in these should help with that.
There are also workloads like compiling code where I’ll take all the extra speed I can get. Every little bit of reduced cycle time helps me finish earlier in the day.
And then there’s gaming. I don’t game much, but the M1 and M2 era Apple Silicon feels sluggish relative to what I have on the nVidia side.
Doubt
I imagine you basically use online models exclusively, and occasionally try out local stuff.
Source: My fortune 20 company tried with M whatever, and the local llms were unusable.
CoreImage - GPU accelerated image processing out of the box;
ML/GPU frameworks - you can get built-in, on device's GPU running ML algorithms or do computations on GPU;
Accelerate - CPU vector computations;
Doing such things probably will force you to have platform specific implementations anyway. Though as you said - makes sense only in some niches.
I think I read somewhere long time ago that Capture One is also using Qt for GUI, though cannot find this anymore, so probably not true.
You mean on your first token. Whats the performance after 500 and 3000 tokens?
I genuinely don't understand why people post stuff like this. People are not informed enough to know you mean first tokens. They are going to make a mistake and buy one thinking they will get 100tk/s.
Are you working for Apple marketing? Do you have post purchase regret? I cannot imagine deliberately misleading people. Maybe you are hoping more buyers build up your ecosystem?
I think the truth is somewhere in the middle, many people don't realize just how performant (especially with MLX) some of these models have become on Mac hardware, and just how powerful the shared memory architecture they've built is, but also there is a lot of hype and misinformation on performance when compared to dedicated GPU's. It's a tradeoff between available memory and performance, but often it makes sense.
That's actually the biggest growth area in LLMs, it is no longer about smart, it is about context windows (usable ones, note spec-sheet hypotheticals). Smart enough is mostly solved, combating larger problems is slowly improving with every major release (but there is no ceiling).
The new tensor cores, sorry, "Neural Accelerator" only really help with prompt preprocessing aka prefill, and not with token generation. Token generation is memory bound.
Hopefully the Ultra version (if it exists) has a bigger jump in memory bandwidth and maximum RAM.
Most stuff ends up running Metal -> GPU I thought
Wondering if local LLM (for coding) is a realistic option, otherwise I wouldn't have to max out the RAM.
This seems even likely as the memory bandwidth hasn't increased enough for those kinds of speedups, and I guess prefill is more likely to be compute-bound (vs mem bw bound).
Linux in a VM would work with the usual caveats. Periphery like the built-in webcam most likely won't work. Getting codecs and DRM to run will be pain and you'll be back to use macOS for that quickly (but that's just standard pain of ARM Linux).
Now it starts at $1699, a $100 bump but comes with a 1TB SSD. Previously it would have cost $1799 for the 1T SSD, so it's a $100 bump on base price but you are also getting 1TB SDD for $100 less than before.
For example, up until MacBookPro M2, MacBookPro M2 came with M2 Pro chip.
However, starting with M3, Apple lowered the MacBookPro MSRP to $1599, but its base configuration was downgraded to M3 chip from M3 Pro. To get the M3 Pro, you had to pay $1999. There's substantial performance between the two.
Same with M4. To get the M4 Pro chip, you had to pay $1999.
Now to get M5 Pro chip, it's $2199. Still a good value, but just saying it's a deviation from the trend.
For those of us with astigmatism it's really night and day experience.
> Even More Value for Upgraders
> The new 14- and 16-inch MacBook Pro with M5 Pro and M5 Max mark a major leap for pro users. There’s never been a better time for customers to upgrade from a previous generation of MacBook Pro with Apple silicon or an Intel-based Mac.
I read as "Whoops we made the M1 Macbook Pro too good, please upgrade!"
I think I will get another 2-5 years out my mine.
Apple: If you document the hardware enough for the Asahi team to deliver a polished Linux experiene, I'll buy one this year!
I still don't have a strong urge to upgrade. I could probably get by on 32GB (like my work-issued machine is) but 64GB is the right amount of headroom for me.
~9 years later, there are a lot of people still using it as their main machine, waiting until we get kicked off the corp network for lack of software support.
for example, let's say the new os depends on m5's exclusive thumbnail generator accelerator, and let's say it improves speed by a 20%.
now, your M1 notebook than on previous OSes uses standard gpu acceleration for thumbnails will not have this specialized hardware acceleration, it will have software fallback that will be 90% slower.
you won't notice it a first thought because it's stuff, fast, but it eats a bit of the processor.
multiply this by 1000 features and you have a slow machine.
I don't know how else to explain how an ipad pro cannot even scroll a menu without stuttering, it's insane how fast these things were on release
This is the important statement. 614GB/s is quite decent, however a NVIDIA RTX 5090 already offers 1,792 GB/s (roughly 3x) of memory bandwidth, for comparison.
You can buy two m5 pro base model for the same price as a single 5090...
In Europe I can get a 128gb mac studio m4 max for 300 euros more than a 5090 (for which you still need to buy a power supply, motherboard, cpu , &c.)
Wish it was Blender though ;)
It's the first time I've ever been so repulsed by a design that I actively avoid it just... out of sheer preference.
The prompt processing sped up.
Not the output generation.
M4 was notoriously slow at this compared to DGX etc.
I'm really wanting to build proper local-first AI workflows at home, and I think Apple has an opportunity to make that possible in a way other companies aren't really focused on, but we need significantly larger memory capabilities to do it, which I know is tough in the current memory market but should be available for a cost.
128 GB maximum.
Sigh.
It's one of those things, yes if I'm spending that much on a laptop I can afford to spend $80 on the adapter too, but does it feel good as a customer to do that or are you souring the experience of buying from you just to earn a few more dollars.
https://appleinsider.com/articles/25/10/15/eu-gets-what-it-a...
In the US they provide one in the box free of charge.
The EU requires that users must be able to buy a device without a charger. It's a huge supply chain challenge to add two variants of every single SKU, one with a charger and one without. So the obvious solution is to sell the charger separately, since you need that regardless, and always sell the device without a charger. You avoid having two variants of everything that way.
Now, you could maybe argue that Apple should default to bundle a charger with your laptop, so that you'd have to uncheck a "bundle charger" checkbox on their website. But do you really care whether your laptop costs $2200 and you can buy a charger for $60 or your laptop costs $2260 and you can save $60 by removing the charger?
You can make an argument that doing it Apple's way hides a price increase. And yeah, that's probably fair. But it's not like Apple is afraid of non-hidden price increases either.
So, if you want one of mine, you can have one. On me. Because I'm fucking drowning in the things and appreciate not having to deal with another one.
Unfortunately, number always must go up (and the rate at which the number goes up, also must go up).
How is that different from the silicon interposer they were using before?
The big change is the two dies don’t have to fabbed next to each other in a single wafer, which is fantastic for costs and yields. But would this affect the interconnect speed somehow?
How would the two be wired together?
Could this mean the Ultra comes back in M6 since it would be easier to fab?
> Testing conducted by Apple in January 2026 using preproduction 13-inch and 15-inch MacBook Air systems with Apple M5, 10-core CPU, 10-core GPU, 32GB of unified memory, and 4TB SSD, and production 13-inch and 15-inch MacBook Air systems with Apple M4, 10-core CPU, 10-core GPU, 32GB of unified memory, and 2TB SSD. Time to first token measured with an 8K-token prompt using a 14-billion parameter model with 4-bit quantization, and LM Studio 0.4.1 (Build 1). Performance tests are conducted using specific computer systems and reflect the approximate performance of MacBook Air.
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