Makes you realize how insane the M5 Ultra Mac Studio is. 1.2TB/s bandwidth 512GB memory. Its rated max power draw is just 480W. And it also has amazing M-series CPUs. It costs less than just one of these GPUs which each take 700W to run.
These GPUs are extremely inflated in price because Nvidia effectively has a monopoly on hardware that is used to train models. Apple Silicon tends to have good inference software available but as soon as you want to train even a YOLO model bits and pieces fall back to software implementations. Try to train an LLM and it'll get even worse.
The M3 Ultra's GPU performance is around a 4070 Ti. The M5 Ultra more like a 5080. They're both amazing deals compared to Nvidia for local inference because of their massive pool of high bandwidth memory. But a single RTX PRO 6000 should be 2 or 3x the compute of an M5 Ultra.
I can't stand Nvidia, but these GPUs are not inflated because of monopoly. They are expensive because demand exceeds supply. That's it. The world wants compute and we don't have enough of it!
I said inflated. They would always be expensive. But they charge more than their competitors per flop and per byte because they’re the only manufacturer that can run CUDA. If they lost that moat their prices would go down a notch.
There is comparison actually. I spent all day researching this a few days ago.
Memory-wise, the RTX PRO 6000 can barely hold two 1M context Qwen 3.8 27B models at 8 bit quantization at the same time. The 512GB M5 Ultra Mac Studio could hold around 14.
At such high concurrency, batch performance is usually limited more by memory bandwidth than compute. The RTX PRO 6000's memory bandwidth is just 50% faster than the M5 Ultra.
So yeah, I think if we are talking about many short context requests, sure. But if you are chewing through a backlog of coding tasks with Qwen overnight, they might actually be comparable.
Im sure in Nov when the M5 Ultra comes out we'll see a lot of interesting benchmarks.
> We currently have 14x nodes of CG480-S6053 ready to ship.
Oh, okay, so this is an ad.
I do still think it's well written and interesting... But if anything, it's just making me more curious about the newest generation of M5 Ultra. (and less and less interested in PCI-E Gen 5 anything)
For the record, I did laugh, I just didn't type it out.
> how do you feel about paying 5% on every token's cost to stripe?
Life is compromise. In a perfect world, I'd love to buy a linux native system comparable to the Mac Studio that could run something in the ballpark of Deepseek V4 flash "fast" at home.
Not only does that not exist, but even if it did I couldn't justify the cost. Between work and home my annual token spend is maybe 3k.
So... paying a 5% fee on a service that allows me to not shell out 10k on hardware at home still seems like a pretty sweet deal.
--
Plus, I still am learning a ton and playing lots. It's hard to imagine anything beating openrouter for that. So much exposure to all the latest LLMs...
These people have zero idea what they're doing. Not a single mention of pipeline parallelism that would actually make the setup useful to run a big model.
I can’t stand it. Very engineering-y over specified formal language around a complete lack of core understanding. Is damaging other people read this and try to learn things from it.
Doesn't really mean anything without a specific use case to guide model selection.
The thing is Qwen 3.8 27B can be ran on far far cheaper hardware. If you're spending the big bucks on these rigs you probably made the wrong choice if you aren't using models that require all of that VRAM.
That's not quite how it works. Throwing Deepseek V4 Flash on 4 of these would net you something like >200tk/s for 16 concurrent requests, that's 600 million _output_ tokens a month. Guess what happens when you use 8
4x RTX 6000 Blackwell cards is a good place to be if you can't swing 8 of them, or if you don't have the power or cooling to run that many. A system based on 4x RTX6K can run GLM 5.3 at NVFP4 precision [1] from a US-standard 120V 20A circuit when derated to 300W, and give you a better pelican than Fable 5.1 [2]. What's not to like?
(Edit: I'm mistaken here, the pelican didn't come from Flash on 4 cards but from the full GLM 5.3 model on 8. But the Flash model is still crazy good for its size.)
Flash will run on 4x, but at the time I ran that test there were no 4-card quants for the full 744B-A40B 5.3 model. There are now, though, with KLD figures close to the FP8 level. I need to do some more benchmarking to see if they live up to the hype.
Pass. When articles keep mentioning models like DeepSeek R1, or Llama 3.1, or Qwen3 32B, it is a pretty robust indicator of AI slop. LLMs love to suggest DeepSeek R1, etc. - training data cut-off?
No person with real practical experience and real use cases will be using these ancient models as examples, when talking about local LLMs.
Utterly awful article. R1? Llama3.1? Not being able to serve larger llms on 8(!) RTX pro’s? You can literally run open weight SOTA models with relative ease. Even 4 GPUs get you there with a bit of elbow grease and compression. Pure slop.
I'm curious whether actual inference workloads actually push to 600W (and not 350W) and what the last 250W get you. Rare is the (generic gpu) workload where I get >5%, some rare light inference benchmarks up to 10%...
Yes that was the second part of my comment. I've been benchmarking many things AI or not on those boards and while one can sometimes get to 600W I haven't seen more than 10% gain for those last 250W. If someone has a workload that gets more from those watts I'd be interested. For now on anything I run (full-capacity, continuous, batched...) capping at 350W seems better in bang-for-bucks when including energy costs (from the GPU + added cooling).
That's a solid 2200 words to spend on operating parameters and conveying state, leaving a generous 700 word window for them to decide and respond in.
When the bonsai/prism 1bit models dropped and I saw how many prompts a minute I could get from a dusty m2 mini I started hooking it up to all sorts of shit, like a traffic simulator that translates the car state/surroundings/immediate goal into text, it responds with a seqeunce of actions defined in the system prompt, which then get translated back into NPC input.
What I was hoping for here was that it would result in fucking chaos, all sorts of stupid decisions and epic car accidents. I cannot overstate my disappointment (and terror) when they were perfectly reasonable, safe drivers. I had to cut the tire grip by 75% without telling them and make them control twice as many cars to delay their ability to respond before I saw anything resembling an enjoyable traffic accident.
Not long-horizon coding but for a lot of other things
like batch processes with structured outputs, quick checks/fixes, making sense of unstructured data etc..
I mean, I can't afford a new F80, but you don't see me going out of my way to post on /r/Ferrari about it. I seriously do not understand the point of these posts.
The topic of the story is running 8x RTX PRO 6000s, so whining about how much they cost or what you could/should buy instead is completely off-topic.
My bad, didn't mean to derail things with my little quip, for what its worth I did read the article and it was an interesting read. Overall I do hope for more smaller models in the future which I can run on my 3090 that deliver similar performance, or for hardware costs to drop in the next few years to prosumer levels.
