Qwen3.8 27B tokens/sec generation speed
Prompt size 8K 64K 128K 256K
RTX 5090 PC 59 51 44 n/a
M5 Ultra 48 39 32 24
M3 Ultra 31 23.5 20 15
I dont' know why people spend huge money on these and Spark. The 5090 is running qwen 3.8 at 200+ tps!! That's 1-2 orders of magnitude faster.
Also: ~30 token/s on GLM 5.3-flash, locally. (That's roughly Opus 4.8-tier. I think).
/meta Here's a CSS filter that stops those nuisance chart animations,
macstories.net##*:style(animation: none !important; transition: none !important)Whereas a hybrid architecture with distinct DRAM and VRAM with sparse MoE, you can leverage two different bit rates depending on the actual need for constant access to common layers versus sparse access to infrequent layers and arbitrage the difference in cost for each of those in distinct classes of hardware.
Now try running that Qwen 3.8 Next model on the 5090 and tell me what TPS you get (hint: it's near 0 since it doesnt fit the 32GB VRAM on 5090 vs the 256 in OPs M5).
https://old.reddit.com/r/LocalLLaMA/comments/1wl06np/qwen38f...
(Note it's a sparse MoE with only 6B active).
These numbers could and should get much better. As an example I can run Qwen3.8-27B-MXFP4 (W4A8) on 2x AMD R9700 that gets 260+ tokens/sec to start and slows down to ~110 tokens/sec over 128k context and can do the max 256k. These are for batch size 1 and throughput goes higher with batching. This is due to speculative decoding, efficient all-reduce inter-gpu compression, and custom GEMM kernels for the specific hardware. Note each R9700 only has 644 GB/s memory bandwidth.
This is with llama.cpp. You can of course use vLLM/SGLang well on these cards and they're even faster. On vLLM w/ NVIDIA/Qwen3.8-27B-NVFP4 baseline has a prefill of about 13,000 tok/s. The baseline tok/s is 72 tok/s, but at mtp7, it's 157 tok/s, and w/ dflash7 that goes up to 215 tok/s. On mtp-bench, DFlash2 gets a hair under 300 tok/s w/ the code_python prompt.
This advantage won't be apparent with a 27B model. The 256GB MS can probably run the newer Flash models locally, something you can't do on a 5090.
I don't think we'll get a successor to the 5090 until late 2028, maybe even 2029. I'm basing this on the launch date of the 5000 series and that we haven't got a midcycle refresh yet. Rumor has it the chips are ready but the 3GB RAM modules are 3-4x the price of the 2GB modules used on the current cards.
Apple should see a Mac Studio major update in 2028. That might even force NVidia's hand. But it's really impossible to say what the state of the market will be 2-3 years from now. It may have completely crashed. I suspect not however.
The interesting thing will be when the bandwidth demands start forcing HBM memory onto these home/enthusiast solutions.
Maybe Apple is an acquisition away from changing that balance.
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nvidia-smi -pl 450 for like a 4% reduction in throughput. I tend to set it around 350W because it's a comfortable temperature blowing on my legs under the desk without warming my office in the summer.
I put together this system two years ago, so it's a little out of date, but it only cost $3000 for the same performance and capability as an Ultra. I don't think I would spend $7000 to save 100W, though.
> Apple's planned M7 Ultra chip is being designed to support up to 1.5 TB of unified memory and to push AI performance toward the class of Nvidia's Blackwell accelerators, according to a new Bloomberg report published by Mark Gurman...
Apple plans to release a base M6 chip this fall for entry-level Macs... a base M7 in the first half of 2027, M7 Pro and M7 Max at the end of 2027, and the M7 Ultra in 2028.
https://www.tomshardware.com/tech-industry/semiconductors/ap...
I'm also curious about any new low hanging optimization opportunities in the kernels for this new hardware.
It's already clear to me that M5 Mac Studio is more cost-effective than anything you can run on open router, assuming decent utilization.
The M5 Mac Studio will be the most cost effective way to run uncensored cyber capable open agents.
An exciting tipping point will be if programmers can get an Astra-Ultra like experience all week with this hardware. That would be a real sense where this hardware exceeds the value of even 20x cloud subscriptions.
That’s like 12 years worth of OpenAI Pro subscriptions
I didn't expect this to make the 5090 to look like a good deal.
It'd be silly to buy the 18k model to run a tiny model like Qwen 27B. You use models like GLM Flash and Qwen Next which won't fit on a single 5090.
This is a great article and bodes well for the M5, but we should expect more like this comparing to other platforms before we truly understand where it fits.
Yes Apple has some of the best hardware out there, albeit overpriced. But the software is such a hindrance and I can't take anyone that states otherwise seriously. If only it had proper Linux support (and the Asahi people do an amazing job but you can reverse-engineer only so many stuff with limited funding, and then you have to do it again for new models). MacOS is good if you just want to have a standard experience, which to be fair is most people. It's good for just setting up an LLM server I guess since the hardware is a perfect fit. I wouldn't touch it otherwise.
I setup an eBay alert and picked up a used M2 Ultra that has delivered good ROI (at least, far better than 15k for comparable-for-my-use-case performance)
For generation speed in isolation, yes.
I ordered the same one for work so I could run more local agents at once (many iOS simulators and Xcode build processes).
The Mac Pro Tower plus the Cinema Display monitor at the time was even more, similar to the Studio M5 ULtra today than the iMac which I believe was an upper middle computer?
$5,700 in 2011 is worth $8,488.46 today
$7,700 in 2011 is worth $11,466.87 today
The cost of Mac Studio M5 Ultra today $9,466.87-$11,466.87
Note: If memory cost was the same as it was two years ago subtract about $2000-$3000 dollars.
Today’s prices are not that far off from top end computers.
- https://sunkcost.ai/s/mac-studio-m5-max-128/qwen3.8-27b-q4/?...
You run uncensored local models where you can ask questions that would get denied by public providers, or questions that you prefer them not to know the intricate details (like your financial planning)
However I also think that Agentic AI is very much not an out-of-the-box solution, local or otherwise, and it takes a high level of technical knowledge to create an effective AI agent. And there's a problem now where most orchestration is fixed on what models are used for what tasks with no ability to weight constraints like cost, speed, and security.
but i wonder how much these token costs are sustainable or not, it may be in the long term cheaper to have your own hardware if token costs go up (and hopefully hardware gets cheaper again)
Entry level serious hardware starts at 100k, and a bit better but still almost-useful grade is 200k (8x rtx pro, plus a nice epyc pairing). Thats the sort of thing a salaried expert lets their employer buy them for sort of serious work.
Anything really serious is well north of 1M - not including the housing and commercial grade mains connection. And at best that buys fast Kimi K3 or GLM.
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Ehh, the actual elephant in the room is:
"why bother with local AI at all when you can lease a GPU for $5/hr?"
To which the answer is you shouldn't bother, unless you have a bunch of money to throw at hobby projects.
Unless you only need the AI available some of the time, $5/hr is pretty expensive. That's an RTX Pro twice a year.
If you're using it for discrete sessions of coding or something, that might make sense for you but if you're using it for an always-on assistant, that pricing kinda sucks.
Perhaps consider some non-offensive language for your comparison?
Ok, as somebody with ADHD I find it offensive because I don't need constant supervision, implying people with ADHD need constant supervision is belittling and just plain wrong. So, I will call out an offensive trope if I see it.
> If you truly are offended, perhaps there is some truth you are reacting to preventing you from truly responding in good faith
No, because if there was some truth to it, I wouldn't be offended. Perhaps stop with the amateur psychology? You're not very good at it.
My technical-expert twin played around with these LLMs, for about an hour, and then correctly reasoned "it's able to be WRONG, faster."
This seems apt. My next LLM machine will be closer to 96gb+ vRAM.
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32GB is still not that much. I would rather get a Spark and have the RAM to experiment with larger LLMs, even if it was slow.
Correct is much more important than fast for me, but if I could get correct and fast, that would obviously be amazing.
No thanks to the "macos value add" that forces you to use Metal while Valve customers frolick in Protonland.
Crossover works on macos, too. So does moltenvk, so does vanilla wine, etc etc. You can run most games without a hitch these days (allegedly, according to /r/macgaming). But I don't play video games so a GPU would probably be better off in some kid's computer.
But of course, Apple doesn't allow that as part of their ecosystem. It's really a privilege to have MoltenVK perform worse than the fanmade HoneyKrisp driver. It's valuable when Apple refuses to sign AArch64 CUDA drivers for macOS. It's exciting to pay Crossover to support half of the library Proton offers for free.
Clearly, I'm some sort of ingrate that selfishly demands the best things, without considering how to accommodate the poor trillion-dollar megacorporation.
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Well, if those are the only two options you can come up with it's pretty clear that this isn't about me or what I am, you have a false model of reality.
> Running very large models on Mac is unusable at 10 tok/sec.
There are plenty of examples of models running at well over 10 tok/sec that aren't viable on the 3090. In fact such examples are found in the review in the OP. Did you not read the article?
I think you're projecting pretty hard with the two options you've listed. Go touch some grass, you seem overly frustrated that reality doesn't meet your expectations.
Inference time is going to be dominated by the low memory bandwidth on these Macs, so a dense model will suffer most. It’s more of an opportunity for large MoE models with a low number of active experts since you can keep all experts in VRAM but not pay the bandwidth cost until they are used.
> you can leverage two different bit rates depending on the actual need for constant access to common layers versus sparse access to infrequent layers
This is an interesting direction that I expect to see more of. But for most models currently you need basically all experts loaded since they are chosen per token.
Apple seems to be researching longer horizon expert caching, where they keep experts swapped in for longer runs of tokens [1]. Other labs are offloading ngram caches but not sure if they’re pursuing anything like this?
1. https://machinelearning.apple.com/research/introducing-third...
I paid $500 for the RAM in Nov 2023 :)
No wonder Warren Buffet gave up and resigned.
That's with CPU offload to a DDR5 6000 RAM though which is around $3-4k at least.
Each PC is probably going to cost ~$6k and you're talking about 8000W of electricity draw. That's going to consume multiple 20A circuits even at 240V. And the electricity ain't free either. A Mac Studio seems to draw ~500W max.
Oh and the Mac Studio has an upgrade route to run 1T+ models too by chaining them together with TB5 chaining. OSX supports RDMA this way. That's comparable bandwidth to the 100Gbps Infiniband option.
So you're talking about $50-60k of hardware and more power draw and more heat for something that will I'm sure beat the MS M5U option but at huge cost. Also, at that kind of price point, I'm likely to get a workstation PC and put 2 (or possibly 3) 6000 Pros in it.
Why Infiniband ("IB")? If it's for RDMA, that is possible with certain Ethernet cards/chipsets as well. Certainly Mellanox, but Broadcom:
* https://techdocs.broadcom.com/us/en/storage-and-ethernet-con...
and Intel as well:
* https://www.intel.com/content/www/us/en/support/articles/000...
Link level flow control or priority flow control needs to be supported on the switch ports as well.
Where are you buying 8 5090s for under $10k? With CPU, RAM, and (checks comment) infiniband hardware???
You're probably looking at a lot closer to $60k when all is said and done, and that's before you hire an electrician to run a sub panel for your homelab...
every company make his own npu (without xai)
probaby in 2028 we will have more concurent firm on market place
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Apple just shifted to N2. They’re not going to be doing another major shift right away.
And TSMCs own roadmap would put your hallucination years away at best for a a product that follows a roughly annual cadence https://www.tomshardware.com/tech-industry/semiconductors/ts...
[1] https://wccftech.com/apple-to-move-to-1-4nm-process-soon-to-...
> Apple's planned M7 Ultra chip is being designed to support up to 1.5 TB of unified memory and to push AI performance toward the class of Nvidia's Blackwell accelerators
https://www.tomshardware.com/tech-industry/semiconductors/ap...
Yeah people don't pay enough attention to those settings IMO. The first thing I do when I set up a new machine (or upgrade my OS) is to restore all my powersaving configs.
For example I've got all but one of my virtual desktops that put the CPU in powersave mode: I don't need max Ghz when browsing the Web, not even on demand. But when I switch to the virtual desktop where my development environment is, then I want power on demand.
Now I don't do it to save the planet: I do it because I love a quieter computing experience (coupled with Be Quiet! PSU and Noctua fans, this makes for a very quiet computer). That it consumes less electricity is a nice side-benefit.
isn’t this very straightforward to do..? I thought batching for Qwen models is already proven out.
> but this would decrease single-session performance even further
Well let’s take Qwen 3.8 27B. Throughput for M3 at 8 agents is 4x compared to single agent. [1]
It’s really not clear to me that 8 concurrent agents at half speed will be worse task completion latency than 1 agent.
And that’s M3 studio benchmarks, not even M5 ultra, and without the many software improvements we will see
If you haven’t tried Qwen 3.8 27B xhigh on a task you might not get the hype. Idk.
If you’ve tried doing this and don’t like it sure, and be specific about what isn’t effective, but let’s not speculate.
[1]: https://omlx.ai/benchmarks/performance/69kzkrv8?utm_source=c...
Local models are definitely not as productive as SOTA, sadly it's not close yet. I do think someday they will be "good enough" to use, but they aren't today. Even the SOTA models barely code well, with Opus 4.5 being the first, good coding model.
That being said, I think it's absolutely imperative that we keep pushing local model performance. We need to continue to advance technology there and ensure that the model labs don't do regulatory capture in the name of "safety" (or anything else).
I think this is true, but also misses that a lot of us are just doing basic flask apps with a react front end. We don't need astra; Something sonnet 4.6 level locally is perfectly sufficient 95% of the time, and maybe 99% of the time.
It's like watching a discussion about cars available to take on a 100km road trip. A new car gets released that is on par with a Toyota Corolla but it is dismissed as completely useless for a 100km trip because it doesn't have the seat massagers and air ride suspension that the new Escalades have.
The reality is that something like Sonnet 4.6 is still amazingly capable for so many programming tasks, especially if you already have some reasonable level of experience to steer it in the right direction.
And if you think Sonnet 4.6 is still worthwhile, then it seems undeniable that something like Qwen 3.8-27B is also worthwhile.
Do they include footguns from pointer bugs?
Not necessarily for MoE
Longer context also slows token prediction proportional to the context size. If it wasn't regularly referenced then there would be no need to keep it in RAM.
Usually the pitch for more memory is "I can run a massive model/context and get my answer in a while instead of next weekend from disk".
I think most people are getting 512 for running Chrome with a bunch of tabs open. /s
Specially since one can pay half right now to OpenAI and sign a 12 year iron clad contract for uninterrupted service delivery of OpenAI Pro.
A more apples-to-apples comparison would be with API cost in OpenRouter at the same tok/s rate for the same models that you can run locally, maybe.
Is there evidence that's true though? I mean gross margins on subscriptions being negative since the API is seemingly very profitable (if the price is compared with the cost of serving very large open models).
As long as there is pressure from other providers serving cheaper models that are somewhat competitive without having to incur any of the R&D costs raising prices will be tricky.
Don't you mean an Apple to NVidea comparison?
I think it goes without saying. And it is eminently evident over last couple of decades that from compute to storage to meals 3rd part providers have saved billions upon billions of dollars to enterprises and individuals alike by providing these essential services.
[1] https://www.macworld.com/article/3238319/mac-studio-m5-max-r...
Plenty of other reasons to get excited about local AI, but I don't think cost is one of them.
And, yes, I know a current local model wasn't going to solve the Navier-Stokes problem, but I'm just using it as an example where privacy might be valuable.
It's a bold strategy cotton, lets see if it pays off for em.
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(Each task needs its own context, but the (e.g.) 27B of constant parameters isn't duplicated).
I get it on Windows systems, at least when someone wants to use Linux-type tooling. But macOS already supports pretty much all of that natively?
For starters, the source code.
Apart from that, for the UNIX part, the source is available for quite a few components:
https://github.com/apple-oss-distributions
notably also the kernel
Why waste time spending years reverse engineering. Wouldn’t it have been easier to raise money for such an endeavor? I hope someone will come along in the future in kindergarten, junior high or high school who doesn’t know any better will try something like this. I’m too old. for such a journey.
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If I switch to Mac OS, I have to sort out a package manager and install all the stuff that's missing, and when it comes to containers... they're just linux VMs. I'd happily cut out the weird proprietary middleman if I could.
I'd be quite surprised if Mac OS alone needs more than 8GB, given that they sell the Neo with 8GB of RAM today.
There's value in knowing what capabilities local models will have on regular hardware in the near future.
Those companies building those big data centers are going to find out that they overspent. Because we’re not going back to the mainframe era, no matter how much OpenAI, Anthropic, Microsoft, Meta or Google would like to.
I thought this only applies to LLM inference providers, but not raw GPU rentals.
A $10/mo subscription to OpenCode Go would have done the job for you.
They have models like Kimi K3, Grok 4.6 , GLM-5.3, Mimo 2.6 Pro (launched today, already available) which are happy to follow your orders without accusing you of being a terrorist.
These models are not 'barely capable', they're contemporary near frontier.
99% of people will use whatever AI is free. The sophisticated, heavy users that are willing and able to pay a lot of money the ones that will be interested in controlling their inference bills.
Today, the sweet spot where an M5 Ultra makes sense is tiny. But we might expect that to grow a lot.
Even if you could get a frontier model, you would not be able to run it on any Mac. So speculating on what M7 or M9 will achieve in 5 years (if we even still exist) seems pointless.
I don’t think Apple is going to lie down and cede AI to the cloud.
How about writing mail to President and senators on AI doomsday scenario if frontier labs do not pace themselves?
That mini model on mac mini would scared to hell to do such thing. It need that rugged frontier model to speak truth to power.
Sam's address will probably be more riveting, imaginative, and terrifying than the last couple of Terminator screenplays. Legislators will lobby him to write the laws for them, and the ghost of Harlan Ellison will threaten to sue him.
I really don't see who buys this, except people who want the Studio for some other reason. But nothing in the story says you want to fill racks with these instead of Blackwell or TPU parts; it's not even close.
Think of a company like Apple moving onto your turf. They’re not going to cede AI to the cloud. They want their part of the pie.
So in 7 years, how much AI will be handled locally on your iPhone. And will you have repaid all the debt on your balance sheet before Apple eats your lunch
It's way too easy for 1T+ frontier labs to ditch Nvidia. So Nvidia will also put effort to make sure there are open weights models and local hardware available.
And Apple will benefit from this too.
5090's are like $0.20/hr
there are lots of people with very expensive hobbies, see sailboat racing for example.
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...actually: get help.
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I don't get these weird parasocial emotional attachments/beefs people have with brands. Talk to a therapist.
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Looking at the article, which you clearly didn't read,the m5 ultra runs Qwen3.8, which fits on one GPU conveniently, at ~20 tok/sec. This is a fucking joke. It will take roughly a minute to generate one code file. Congrats if you want privacy I guess, but for straight up coding, you are better just using cloud models.
Meanwhile, I have an $800 mini PC, $200 Occulink gpu dock, a $2000 3090 and a $300 power supply, and I can run Qwen at over 100 tok/sec prefill, not to mention insanely quicker during inference. So its pointless to spend Mac M5 Ultra prices on Apple shit when they can have something much faster for cheaper
The whole thing of "well I can run bigger models that don't fit on a GPU" is either paid Apple advertising, or you are just an igorant fanboy.
So I ask you again, which one are you?
No thanks, you're not in a position to do that clearly.
> Since you clearly don't use local llms
I do, probably a lot longer than you have actually.
> anything under 100 tok/sec is USELESS
Objectively wrong. You sound like you're really behind and you're so myopic that you think coding is the only use case for local LLMs. I'm a professional software dev and that's the least interesting use case of local LLMs.
> Looking at the article, which you clearly didn't read,the m5 ultra runs Qwen3.8, which fits on one GPU conveniently, at ~20 tok/sec.
You clearly didn't read the article or have reading comprehension issues. The model is Qwen3.8-Flash-Next 4 and 5-bit quant, neither of which "conveniently fits on one GPU". Sorry that your hardware doesn't live up to your own delusions and can't even run Qwen3.8-Flash-Next at 4/5 bit quant. You are taking the Quen3.8-27B numbers, something that the article isn't really that concerned with, and trying to make it fit into your narrative.
> So I ask you again, which one are you?
Well I'm someone that suggests that you should touch some grass and reevaluate your personal issues. You seem angry. Perhaps it's best to figure your own issues before trying to figure out why people are excited about Apple hardware for local llms. I am sure the people that need to interact with you in society would be very grateful if you took the time to do this.
A) He literally says "I tested a different Qwen model for the comparisons between Mac and PC." The model he tested has to fit on one GPU, otherwise the inference is dogshit slow as you are offloading results to ram. If you ran any amount of local inference, you would know this. Considering that Qwen3.8-Flash-Next Q4 is still 100gb, there is no realistic way to run this with a 5090. The model that was run was this https://ollama.com/library/qwen3.8:27b. And the speed of that model on a 5090 in terms of tok/sec is not 60 lol.
B) If M5 ultra runs 40 tok/sec on qwen3.8:27b (and lets assume its the mlx version to gain a performance boost: https://ollama.com/library/qwen3.8:27b-mlx), you have to be delusional to believe it can run 100gb models at 100 tok/sec lol.
As a bonus, in terms of use, its pretty well known that Qwen models are RLed to chase benchmarks. Check out https://huggingface.co/Qwen/Qwen3.8-27B versus https://qwen.ai/blog?id=qwen3.8-flash-next, using different benchmarks the 27b outperforms the flash next on agentic coding. But it matches it in other areas pretty well. So tell me again why you need 100gb models running dogshit slow at peak ~20 tok/sec?
It is so incredibly sad how hard you try to sound intelligent. But thats on par for the course of any person hyping up apple products, throughout apples history.
Considering that Apple probably doesn't want you to engage in this level of pettiness for their advertising posts, you have outed yourself to be #2. And Im not angry at all lol, you keep doing what you do, people like you in the industry are the reason I can work 8 hours a week and still get get paid a lot while being reviewed highly.
If you look at the pricing of a full (x86) AI workstation you'd need around the nvidia GPU, you'd approach $10k easily (and be using a ton more wattage too).
I'm also a huge fan of local models and think it's absolutely imperative that they continue to advance so we can move off of the Anthropic/OpenAI hosted models. It's important to accurately asses where we are in that journey though.
The internet is still alive
I appreciate the reference to RUSH: Red Barchetta in the final line.
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Might be if RAM prices get much more reasonable its gonna be 1/3 of the price, but it's very much possible its gonna be half or more.
And if you're buing Mac Studio and not some AI-only locked down board it's possible to reuse it for other purposes.
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Strictly speaking, Apple can claim to ship a UNIX® operating system:
disable System Integrity Protection, enable the root account, enable core file generation, disable timeout coalescing, mount any APFS partitions with the strictatime option, format your APFS partitions case-sensitive (by default, APFS is case-insensitive, so you’ll need to reinstall), disable Spotlight, copy the binaries uucp, uuname, uustat, and uux from /usr/bin to /usr/local/bin and the binaries uucico and uuxqt from /usr/sbin to /usr/local/bin, set the setuid bit on all of these binaries, add /usr/local/bin to your PATH before /usr/bin and /usr/sbin, enable the uucp service, and handle the mystery issues listed in the four Temporary Waivers.
kokonokko1337 already said it was good enough to run LLMs, presumably RunSet isn't saying the source code is needed to run an inference server.
I would love to find an excuse to buy a 10,000 dollar machine! But I cant find one yet. My current cloud bill is in excess of 400 USD per month. Just can't achieve frontier model capabilities locally.
You are comparing dense models to MoE. You can't just take a qhen3.8:27b, a dense model, and extrapolate that math to make your arguments for the MoE model being discussed in the review.
All this takes is a simple bit of research, it's not like this author is the only person that's ever used qwen3.8-flash-next or another MoE on a mac ultra. https://tbreak.com/mac-studio-m5-max-review-local-ai/
> people like you in the industry are the reason I can work 8 hours a week and still get get paid a lot while being reviewed highly.
LOL. No offense but you sound like a carriage return. I'm pretty sure you sit at your computer hitting enter on a mechanical keyboard next to your fabulous mini pc living in 2025. You couldn't even imagine another use case besides being a carriage return for a local llm.
Correctness matters much more than speed to me, but if I can get both, that’s obviously very interesting.
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