Was my $48K GPU server worth it?(rosmine.ai) |
Was my $48K GPU server worth it?(rosmine.ai) |
In comparison to just spending for tokens, the tokens would have been much cheaper and much much faster. I've been running against Gemma4:31b, Qwen3.5 and 3.6, and getting local LLMs to solve AMC 8/10 math questions and it's about 10-100x slower than just doing it online. When I tried it with ChatGPT late last year, it took about one night and $25 to solve about 1000 questions. Using my RTX 6000 and M3 Ultra and Gemma4:31b on both, it answered about 40 questions in 7 hours and I haven't checked how good the answer is yet. At 800 watts (600 for RTX and 200 for M3 Ultra) and running for 7 hours, it solved around 40 questions.
At the very least I'm going to try to sell my M3 Ultra if I can find a reliable place to sell it without getting ripped off by scammers.
After my last run, I'm going to wait for the new case I ordered to come in and cannibalize my kid's PC that we built beginning of this year to form an entirely separate computer. And then figure out better ways to deal with the heat, especially with summer coming up. I'll have to play around with undervolting and running vents directly outside my house to see if that helps.
But the trend here is interesting. I think by 2030 you'll be able to buy fairly cheap hardware that is currently $10k+. I don't know what this does to the trillions invested in AI data centers because the next NVidia architecture after Blackwell will essentially half the value of purchased cards overnight.
I'm not convinced Apple has yet pivoted the Mac Studio line towards this market and the expected M5 Ultras in Q3 2026 will likely be an incremental improvement rather than big leap forward but I'd like to be proven wrong.
For a lot of research questions 6 GPUs is even overkill.
It’s one of the reasons I’m skeptical of the “trillion dollar supercluster” idea [0]. I think what we need is more reasonably smart people investigating medium-sized problems. A “GPU middle class” you might say.
[0] https://situational-awareness.ai/racing-to-the-trillion-doll...
It just scares me to own a box that is $48K in my house, especially if it breaks, or gets stolen.
No wonder gamers hate AI bros.
- https://www.williamangel.net/blog/2026/05/17/offline-llm-ene... - Discussion: https://news.ycombinator.com/item?id=48168198
Because that wasn't what they claimed to research?
>> for inference it's definitely not worth it.
But yes, for pure inference, the M5 Max Macbook Pros probably aren't there yet. They have other utility though of course. And you can get 64GB and 128GB MBPs at a discount. Micro Center currently will let you buy a 64GB M5 Max MBP for under $4k currently, for example.
Or, for a person who did have a great way to monetize the same workload they’d probably find a lot of value in reading this post.
While I'm skeptical that there is much of a moat, at least for the large players, it should at least hopefully set rosmine up with for the next job :)
It does seem to fix the current biggest issues with using LLMs for writing at various publishers. If you're The Economist, you have a very specific house style and you have a decent corpus of articles written in that style. At least on my reading of it, rosmine can use DFT to get a model to closely match its outputs, in terms of the language quirks that are generated, to that of the corpus it is fine tuned on. ie it will very much match the house style, particularly as it is used in writing, vs giving a system prompt to an LLM that has some Economist articles in its vast training set, and telling it to write in that style- it will do an ok job, but still exhibit LLM language quirks despite itself. Even if you feed it the specific "style guide" that they give their authors, I dare say the reality of their writing is the best place to learn, and it sounds like DFT can ground the writing of a model in a specific corpus like that.
[1]: https://rosmine.ai/2026/05/18/fixing-llm-writing-with-distri...
They do it well enough that it'd take really good output to beat.
(I would assume they haven't made a lot of $ off of this, if nothing else because they've only just put out that post and demo. They do seem to have produced a model that doesn't sound very LLM-y to my ear, though it also seems rather weak for its size.)
Cynical take: They made an LLM that can bypass existing AI slop detectors.
Realistic take: They found a research problem they found interesting, dumped a bunch of capital and sweat equity into and (claimed to have, at least) found a solution. Neat!
"I spent a long time trying high risk/high reward experiments and failing. But now I have something good. I’ve solved a major problem with LLMs. And I’m launching next Monday so we will soon see if it’s actually a breakthrough or just LLM psychosis "
Maybe ai companies today have some bounty program?
Cloud is optimized for development velocity but its nature of high margin business eventually makes on-prem more promising
It could be too late but it might be worth looking into tax saving if you have a business. Depreciation of asset is a loss and may deduct your income. (I'm NOT a tax expert)
As the author notes, there are also electrical/wiring issues that cap how much compute gear you can run in a space not designed for it. I suspect a standard 20A 110V circuit can probably handle 2x RTX 6000 Pros. 15A probably can but that requires more research. Anything more than that and you're using multiple circuits, which has issues, or you need an upgraded circuit (eg 40A 240V) with all that entails (eg heavier duty cables, custom plug, etc).
Genuine question; would anyone here recommend any specific motherboard to best utilize these cards?
I myself run with gigabyte trx40 aorus xtreme, but since it's regular threadripper (not pro) with 4 GPUs 2 of them will run at x16 and two of them at x8 speeds
The Ada has a memory bandwidth of 960GB/s. The Pro has 1.8TB/s and about 40-50% better performance so is at least equivalent in processing power, much better in memory bandwidth (important for inference) and can hold larger models on a single card.
I've considered buying a rig with 1-2 6000 Pros for similar reasons but I want to see what happens with this year's Mac Studios with a likely M5 Ultra. Macs have a shared memory architecture whereas NVidia segments the market based on max memory where the biggest consumer card (RTX 5090) has 32GB of VRAM but still excellent memory bandwidth (1.8TB/s). A RTX 5090 rig will still trounce a Mac Studio seems to be the conventional wisdom. Despite being able to hold larger models and being able to chain Mac Studios on TB5, their lower memory bandwidth (~900GB/s) and lower overall GFLOPS mean they still come out behind.
That being said, the current Mac Studios are relatively long in the tooth, being released in 2024.
I'm still not sure any of this is really wroth it because things are still changing so fast. I think there's a decent chance of a number of large AI companies going bust in the next 2-3 years such that you'll be able to buy enterprise AI hardware at cents on the dollar, a bit like how Google bought data centers in the post-dot-com crash.
But anyway, nowadays I'd be looking at the RTX 6000 Pro as the sweet spot, having anywhere from 1-4 in a single server.
The electricial issues the author mentions are interesting. I hadn't really thought about the max amperage on a residential circuit. In a DC, these would typically operate on three phase power and much higher overall amperage. I wonder if there's a device you can buy that can combine multiple residential circuits into a single power source for a server this power hungry?
I don't think anything compares to the nVidia chips at all.
Is this the best general-purpose choice as of 2026 with $50k for training, fine-tuning and running large open models?
Edit: I now see the author was in an apartment and couldn't do this, so I concede this is not responsive here.
:( you paid a professional pc builder and you weren't told this?
There is no specs in this blogpost regarding cpu/motherboard choice, but if you go with threadripper pro they have 128 pci-e lanes for some time now, so using all GPUs at full speed shouldn't be a problem
edit: Hm, finding mixed information online on whether that's still supported or not. Apparently it was removed in workstation GPUs.
At the time he put this rig together, there weren't a lot of open-weight LLMs that could run well on 6x48=288 GB, so it probably wasn't a huge loss. There still aren't, really.
Right now I'm in the process of cramming Blackwell cards into an old DDR4-based Milan server, where the important thing is to be able to run large models at all. The GPU fans alone burn over 400 watts at full throttle.
The idea is obviously to be running the LLM on your work laptop. As a developer I'd need a laptop with 24GB of RAM for work anyway, and 48GB, which is enough for a very good quant of Gemini, is just $400 extra.
If your goal is to say, write science fiction, their reversion to classic LLM-isms, is really distracting and is what makes people say from a glance that it was written by an LLM. You basically can't use them at the moment in any real "natural" long-form writing. Everyone will call "slop" pretty quickly on the current frontier models.
Rosmin's DFT paper is worth a read.
The server is going to live in the garage, so I'm not that concerned with noise. But I had no idea what to expect when I flipped the switch for the first time. It sounds like something out of the Book of Revelation. No way, no how could something like this be used in an inhabited area.