Have a 5090, and yes it's very fast. But it's like the worst ADHD team member and requires constant supervision and review from larger models. It's context size on-card is good for super, suuuuuper shallow precision work.
The gb10/spark on top of it, that thing can refactor enormous monorepo architecture. The time it takes the 5090 to compact, reiterate and execute a plan is often the same time as the gb10.
Exactly; when I first got my RTX 5070 Ti (16gb, to game with!!!, upgrading from VEGA56), I loaded then-latest Qwen3.6 (~30B, cannot remember exactly). My only prior LLM experience was with models <8gb, primarily llama3.1.
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.
Personally I think the price is way too high right now. It’s a power hungry gaming GPU. The efficient single card equivalent would be a 4500 Blackwell which launched at about $3500. Or you could get a 9700 32GB or an Arc B70 for well under $2k, today. You only buy a 5090 if you want absolute speed.
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.
A) the macos value add is enormous if you have any investment in the ecosystem, B) for me at least a GPU is completely useless for anything but being a token generator.
> 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.
A GPU would be better-off attached to your Mac in an eGPU enclosure. There is not a single Apple Silicon GPU on the market that leads the industry in prefill, decode or power efficiency.
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.
A 5090 has a 1.79TB/s memory bandwidth. Qwen 3.8 27B NVFP4 is 22GB. You cannot generate tokens faster than the weights can traverse the GPU memory, so that makes max generation speed without MTP to be 81T/s. Say MTP is giving you 0.5 acceptance rate (very good), that is 1.5 * 81 is 121T/s. Even with a perfect acceptance rate you would only get 162T/s.
Off the top of my head, I'm guessing we're missing sparse attention. But I'll run your challenge through and see where the gaps are. I promise I'm telling the truth :)
brother my point is they don't care about their product offerings outside of their phones. this post/thread is about one of their product offerings which is not a phone which is inferior to their competitors'. simple.
This 1000%. Data centres don't equate to medium sized labs and businesses. A stack of Macs is up and running without digging trenches, an electrician on staff and a department of PhDs to justify the spend.
It's likely that a stack of Macs will draw more power for slower prefill/decode than equivalently priced Nvidia GPUs. If power efficient inference is the goal, Macs are a non-starter.
I guess it is possible, but Apple has had very vocal fans for decades. I suspect, rather than astroturfing, it is just people who are in their ecosystem.
Running very large models on Mac is unusable at 10 tok/sec. You get more average inference over the day using free Google Gemini.
And for the price of a Mac that can run a large model, you can get 2 3090s humming along running a small model so fast that it can simulate a lot of the behavior in large models just through sheer number of context it generates. For example, editing code means that by the time your large model on your Mac is finished writing a file, the smaller models have generated the code, written the code to file, ran it, and debugged any issues.
So given that, which one of these is true about you?
1. You are paid by Apple to push marketing on HN
2. You are a hardcore Apple fanboy and just think that owning a Mac studio is a flex
> So given that, which one of these is true about you?
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.
Since you clearly don't use local llms, allow me to educate you - anything under 100 tok/sec is USELESS. When you are coding, the idea is that you want to have a system that can generate files fast, hopefully correct on the first try. Cloud models do this. Local models, by nature of having less parameters and more quantization, often require more guidance and repeated inference to get it right. The antigenic harnesses that people set up around local llms leverage this.
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.
can confirm.
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.
Have a 5090, and yes it's very fast. But it's like the worst ADHD team member and requires constant supervision and review from larger models. It's context size on-card is good for super, suuuuuper shallow precision work. The gb10/spark on top of it, that thing can refactor enormous monorepo architecture. The time it takes the 5090 to compact, reiterate and execute a plan is often the same time as the gb10.
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How are you deciding which work to send to the 5090 vs a frontier model, or making the two work together nicely?
Correct is much more important than fast for me, but if I could get correct and fast, that would obviously be amazing.
People don't buy Sparks and M5 Ultras to run a 27B model - you buy it to run an MoE model like Qwen Next which this M5 excelled at.
Exactly; when I first got my RTX 5070 Ti (16gb, to game with!!!, upgrading from VEGA56), I loaded then-latest Qwen3.6 (~30B, cannot remember exactly). My only prior LLM experience was with models <8gb, primarily llama3.1.
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.
Once I get some kind of settlement after getting beaten up by a cop my first purchase will be some RTX Pro 6000s.
Is a 5090 still cost efficent when it is (currently) unobtainable? Or when obtainable only at current prices (min. $6500 USD)?
Personally I think the price is way too high right now. It’s a power hungry gaming GPU. The efficient single card equivalent would be a 4500 Blackwell which launched at about $3500. Or you could get a 9700 32GB or an Arc B70 for well under $2k, today. You only buy a 5090 if you want absolute speed.
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.
You can lower the wattage and it doesn't lose much perforamnce.
Because you can run Qwen 3.8 Flash Next, Laguna S 2.1 and other medium-sized models that simply don't fit on a 5090?
A) the macos value add is enormous if you have any investment in the ecosystem, B) for me at least a GPU is completely useless for anything but being a token generator.
> for me at least a GPU is completely useless for anything but being a token generator.
No thanks to the "macos value add" that forces you to use Metal while Valve customers frolick in Protonland.
> 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.
A GPU would be better-off attached to your Mac in an eGPU enclosure. There is not a single Apple Silicon GPU on the market that leads the industry in prefill, decode or power efficiency.
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.
A 5090 has a 1.79TB/s memory bandwidth. Qwen 3.8 27B NVFP4 is 22GB. You cannot generate tokens faster than the weights can traverse the GPU memory, so that makes max generation speed without MTP to be 81T/s. Say MTP is giving you 0.5 acceptance rate (very good), that is 1.5 * 81 is 121T/s. Even with a perfect acceptance rate you would only get 162T/s.
It really does get it, because MTP is usually run at "3 token" depth. It's pretty shocking to watch
Off the top of my head, I'm guessing we're missing sparse attention. But I'll run your challenge through and see where the gaps are. I promise I'm telling the truth :)
I think you’re missing that MTP can predict more than 1 token in advance.
Qwen3.8-Flash-Next is pretty damn worth the extra ram you need.
same reason they spend huge amounts of money on rolexes when seikos work better (the tech crowd isn't immune from vanity).
If you seriously think apple products are nothing but a status item, you're deluding yourself and probably have been for decades.
If you seriously think apple cares about anything other than cell phones, you're deluding yourself and probably have been for decades.
...did you mean profit? I don't think they're manufacturing iphones just on the hope they delight you. This is also true of Google et al.
I don't get these weird parasocial emotional attachments/beefs people have with brands. Talk to a therapist.
brother my point is they don't care about their product offerings outside of their phones. this post/thread is about one of their product offerings which is not a phone which is inferior to their competitors'. simple.
My M1 Pro MBP is 6 years old and continues to be the best computer I own, so if that’s Apple not trying, god help everybody else once they do.
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https://forums.macrumors.com/threads/we-need-to-discuss-the-...
They've been selling phones for less than 20 years at this point? Though I suppose 1.9 is not equal to 1, so it gets the plural.
This 1000%. Data centres don't equate to medium sized labs and businesses. A stack of Macs is up and running without digging trenches, an electrician on staff and a department of PhDs to justify the spend.
It's likely that a stack of Macs will draw more power for slower prefill/decode than equivalently priced Nvidia GPUs. If power efficient inference is the goal, Macs are a non-starter.
So if it isn't a comparative ability, now it's a power cost issue? This reads like goal post moving.
Oh, it's absolutely both. The power you waste waiting for TFTT on prefill will absolutely compound at the "medium sized labs and businesses" scale.
Both are probably single-token decode performance, which is reasonable to show. Otherwise agree RTX 5090 should shinebetter with NVFP4.
The issue is that the moment you want to run the more capable models that will no longer fit in a single 5090's memory, performance falls off a cliff.
... or with llama.cpp with MTP.
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I guess it is possible, but Apple has had very vocal fans for decades. I suspect, rather than astroturfing, it is just people who are in their ecosystem.
Tok/sec is 0 on a 3090 for most of the models that the mac can run
Running very large models on Mac is unusable at 10 tok/sec. You get more average inference over the day using free Google Gemini.
And for the price of a Mac that can run a large model, you can get 2 3090s humming along running a small model so fast that it can simulate a lot of the behavior in large models just through sheer number of context it generates. For example, editing code means that by the time your large model on your Mac is finished writing a file, the smaller models have generated the code, written the code to file, ran it, and debugged any issues.
So given that, which one of these is true about you?
1. You are paid by Apple to push marketing on HN
2. You are a hardcore Apple fanboy and just think that owning a Mac studio is a flex
> So given that, which one of these is true about you?
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.
Since you clearly don't use local llms, allow me to educate you - anything under 100 tok/sec is USELESS. When you are coding, the idea is that you want to have a system that can generate files fast, hopefully correct on the first try. Cloud models do this. Local models, by nature of having less parameters and more quantization, often require more guidance and repeated inference to get it right. The antigenic harnesses that people set up around local llms leverage this.
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?