In the last year, I have bought an M3 Ultra Mac Studio with 512 GB, a Macbook Pro M5 MAX with 128 GB and an RTX 6000 Pro. I have spent around $25k so far, not including electricity. I figured worst case scenario I can sell them in the next year and only take a haircut as opposed to losing my entire investment.
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.
I administer a simple AI server in the office, which just uses a single RTX 5090 but is able to serve ~80 people throughout the day. I'm impressed by Qwen3.6-27b's capabilities in agentic coding/tasks so far. Devs say it's not much different from Sonnet 4.6 on many tasks (sometimes it even outperformed it), 40-60 tok/sec, up to 260k context. The server cost about $10k with all the bells and whistles.
I spent a lot of time researching/adding/benchmarking many custom modifications to the software stack and its settings to make the server optimally handle the load with just 1 RTX 5090 without losing quality, but it's still not enough, and the wait times in the queue are getting longer. We're at the limits of the hardware, and I'm out of tricks.
The experiment was kind of a success, and the CTO agrees we should scale it. With our own infra, we could run agents 24/7 on everything. Currently, a lot of use cases for the cloud providers are completely blocked by PII/trade secret concerns (our infosec department doesn't buy the "zero retention" promise), plus you don't have to think about billing/budgets/etc. anymore.
Now I can't decide how to scale it. On one hand, I'd like to run larger models. And we have the budget to buy, say, 8xH200. But in many benchmarks, the larger models that do fit in 8xH200 comfortably and can serve many parallel requests with acceptable speed/quality don't seem to outperform Qwen3.6 that much in agentic coding/tasks to justify the price.
So another option is just to buy a bunch of RTX 6000s and scale horizontally instead: run a copy of a midrange LLM like Qwen3.6 on each GPU. It's cheaper and easier to scale/replace, but then we'll run into problems running larger models in the future if we have to, because of no NVLink support (say, if Alibaba & Co. stop releasing ~30b models and/or ~30b models start falling behind 400b+ models considerably)
Does anyone here have experience running large models in a multi-GPU setup with several RTX 6000s in a high-concurrency regime and with large context lengths? (something like Deepseek 4 Flash, Minimax 2.7 etc.)
> our infosec department doesn't buy the "zero retention" promise
They are wise to be skeptical! It is neither a promise nor zero data retention.
Look at Anthropic's Zero Data Retention policy -- and remember, this is the policy that applies to the exclusively eligible enterprise partners who can even qualify for a ZDR agreement with Anthropic:
> When ZDR is enabled, prompts and model responses generated during Claude Code sessions are processed in real time and not stored by Anthropic after the response is returned, *except where needed to comply with law or combat misuse*.
> Even with ZDR enabled, Anthropic may retain data where required by law or to address Usage Policy violations. If a session is flagged for a policy violation, *Anthropic may retain the associated inputs and outputs for up to 2 years*....
This means that Anthropic is actively inspecting all of your data with machine learning classifiers. When the usage is flagged for whatever reason as violating any aspect of Anthropic's Usage Policy, then they get to keep your data for 2 years, with no apparent limitation on what they can then use it for.
Crucially, you have ZERO guarantees about the sensitivity or specificity of these classifiers. For all anyone knows, Anthropic is silently flagging 75% of queries and retaining the data.
I have a 5090 machine sitting idle that I'm considering turning into a machine for my own small team (3 devs).
Are you willing to share any lessons learned, etc. that I could make use of? We are evaluating paying for a SOTA sub or trying this, and the talk about Qwen3.6-27B makes me want to try deploying this machine.
Thank you for the insight. This makes me feel confident, the L40S we are about to acquire with 48GB VRAM for engineering application should be useful for agentic coding as well.
Wouldn't that be a fairly ideal setup for layer parallelism? That doesn't need the high-performance communication of tensor parallelism, and the high-concurrency regime would make it easy to keep the pipeline full with microbatches. You'd also be able to scale out your KV cache storage since that naturally splits layer-wise.
Qwen 3.6 27B is fine but it's not in the same ballpark as GLM-5.1 or Kimi K2.6.
If you truly want to scale up, you should get the 8xH200 with NVLink.
Does anyone here have experience running large models in a multi-GPU setup with several RTX 6000s in a high-concurrency regime and with large context lengths? (something like Deepseek 4 Flash, Minimax 2.7 etc.)
For what it's worth, I've been seeing ~100 tps with 4-bit MiniMax 2.7 on two RTX 6000 boards, just running under llama-server without any optimization effort at all. I have no serious long-context experience with that setup, but at 30K context it's still above 90 tps.
If you are happy with Qwen 3.6 27B, I would personally switch the 5090 out for 2x RTX 6000s and keep running 27B. That will give you ~2x your current throughput with a lot more headroom for multiple users. More important, it would buy time to see how things develop over the next few months before you spend a whole lot of money.
This article appears to lack any reason for "needing" this beast, or any real comparison with alternatives, both of which are required to answer the question posed in the title. It's a summary of how much they spent and some light anecdotal comparison to what they might have spent on cloud services, but clearly they didn't do an exhaustive hunt for value.
The real question is whether or not they could have done whatever it is they did with less hardware. Is there a business idea here that could have been proven on cheaper hardware that could be upgraded as demand increased? Is the expected ROI there based on future earnings?
Absent any indication that this was needed in the first place, I can only conclude that it wasn't worth anything.
At the end of the article, the author has this to say:
> UPDATE: Launch was a success! 400K+ views, and multiple companies reached to use my IP. Read more here[0]
[0]https://rosmine.ai/2026/05/18/fixing-llm-writing-with-distri...
Post-hoc justification. There's no analysis of whether that level of hardware was necessary to launch, only that they did get that hardware and did launch.
Looking at the GPU utilization graph, it certainly seems like the hardware was saturated for many days/weeks on end.
Was it worth it to spend that amount up front, yak shave while building the system, etc. vs. pay for cloud GPUs? Probably not in terms of dollars, when their time is also valued in dollars.
Was it worth it for this person? It seems, unequivocally, yes.
Their more recent post seems to suggest it was worthwhile. https://rosmine.ai/2026/05/18/fixing-llm-writing-with-distri...
Abstract/TLDR: LLMs are notoriously formulaic at writing, overusing certain tokens or phrases. I show that models trained with SFT fail to match the distribution of the training data by using Maximum Mean Discrepancy (MMD), Judge Model Quality (JMQ), and L2 Token Distribution.
That is like saying my new restaurant was a success, therefore powering it with a generator was better than connecting it to the grid.
The raw infra being local didn't enable any of that. Now if was building ASICs at TMSC that would a different thing because you'd then be using something different locally.
Idk if this turns into revenue or some financial metric but even if it does and it was a good outcome for author, it still says nothing of risk. What if he loses his timing opportunity / gets beat to market because he's unnecessarily futzing around with hardware? AI is rapidly advancing and he spent 2 years on this to save what was probably <2 months of faang income. There's multiple other angles I could dissect this from a risk perspective. I'm all for taking risks, but at least acknowledge them and preferably measure them as part of making big decisions like this to save a little bit of cash.
Let’s be clear, though, FAANG (as someone who has spent an awful lot of my life working at FAANG) was pays well but crushes your soul. There was a time, a very long time ago, where it didn’t, and there are the soulless soul crushers that love it there, but I would rather futz around with a mid range cars worth of hardware and be happy than spend a moment longer prostituting my soul for their money.
There was a time in this industry that it paid about as well as an accountant and people did it because they loved what they did. Then the money flooded in, a bunch of people switched majors from business to CS, washed out in industry, got their MBA, and became product managers and engineering managers and sucked all joy from it. God bless those that find that joy again.
UPDATE: Launch was a success! 400K+ views, and multiple companies reached to use my IP. Read more here
It seems that he managed to get what he wanted from the hardware and I'm happy for them.
He said something interesting at the beginning of his post, he compared the cost of the hardware to the cost of his time based on his FAANG salary. Which is an interesting way to think of this, but the rest of the article didn't make me understand if at the end he did save money/time based compared to just rend on the cloud.
Also, outside of the power cost, hardware has other costs too, you need to operate it, maintain it, set it up, etc. all that require time. I mean, even the process of figuring out if it had a good enough ROI compared to cloud, takes from your time (collecting data, analyzing data, etc etc).
This is interesting but I am unsure how you make money out of this home setup, I would imagine if one would be offering consultancy to a business the business would make their own equipment/infrastructure available, which would also give a better control of their data. But perhaps I am thinking this because I am thinking about very big companies. Then, on very small business I don’t see they having the use case with the budget to match the need. So is this for specific services for medium sized businesses? Can you explain this a bit?
At then end they briefly mentioned how they started a service to post-train LLMs on producing more human, less formulaic-obviously-AI text.
I did the math at least on a Macbook pro, and for inference it's definitely not worth it.
- https://www.williamangel.net/blog/2026/05/17/offline-llm-ene... - Discussion: https://news.ycombinator.com/item?id=48168198
That's the case with Self-hosting anything. It is the privacy that matters.
It's comparing laptops to dedicated GPUs in a server environment. The best comparison would be the Mac Studio but the current release is almost 2 years old at this point. We'll see what a likely M5 Ultra Mac Studio looks like, probably in Q3 this year.
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.
Except this math is 10x too high (unless accelerated depreciation is all of it) - a million tokens at 28 tokens/sec and 75W and 20c/kwh should cost $0.15 not $1.50. (And less with MTP.)
Why didn't you take into account batching, input tokens, different costs of electricity, and the fact that a laptop can still hold a decent % of its resale value, and is useful for many other tasks than running an LLM?
> Why didn't you take into account [...] the fact that a laptop can still hold a decent % of its resale value, and is useful for many other tasks than running an LLM?
Because that wasn't what they claimed to research?
>> for inference it's definitely not worth it.
Who is going to buy a $4299 M5 Max MBP with 64GB of RAM just to run Gemma 4 31b? Firstly you don't need 64GB for that model. Secondly if you want a machine that sits in the corner and does nothing but LLM inference, you don't buy a MacBook Pro, you buy some GPUs which are going to cost you a fraction of that (~$1k for ~64GB of VRAM is possible). The people buying Apple Silicon for inference general aim for the Mac Studios with enormous amounts of RAM (128-512GB), to run very large models.
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.
> Firstly you don't need 64GB for that model.
You might need that to run it with a longer context, KV cache size is a known issue with that model series.
What quant? You should have no problem running it at Q4 with 256K context, Q5 or Q6 even although maybe not at full context. I can run Q4 on a 4090 with just 24GB VRAM.
"The point of buying the server wasn’t to save money, it was to build something cool." In the end, this is always the real answer - one that I'm sure we can all agree is the 'correct' one too.
Sounds fun/stressful/rewarding. I'm most interested in the update at the end though 'Launch was a success! 400K+ views, and multiple companies reached to use my IP.' I too, like probably 1 in 5 of the people reading this, think I have figured out some major problems with LLMs (context and computation research) but have wondered the best way to 'release' and get value out of it. I can see training being a little easier in that you release weights against a known model arch but not the training code. Wy stuff is all custom layers though. Any thoughts on a release strategy where you need to release the layer code for people to see test weights/the benefits?
My first advice is to have a test set with clear improvement, and a clear "wow" demo use case. There are lots of "breakthroughs" that seem good but aren't (e.g. some new architecture that doesn't mask past tokens correctly and leaks information), so people will assume it is wrong. To prevent this, you need to be extremely rigorous in your launch materials. If you can make it into a product that people can try out themselves, that goes a long way. You don't need to open source any code (I haven't yet) if people can try it out some other way like a demo website. Good luck! Ping me if you want to chat more
Other things people spend "too much money" on:
- muscle cars, with all the stuff, driven occasionally.
- boats, that don't get taken out much
- gamer x, where x=system or laptop or keyboard or mouse or desk or glasses or mousepad or speakers or ... usually with "> too much RGB"
- children
$48k for something constructive even if ai related? no problem, refreshing even.
One of these things is not like the others. If you don't spend the money on one of them, you can get a visit from government officials that might decide to take that "item" from you. You'd also be a worthless human to spend that money on the other 3 while not on the one.
I have profited richly from my children, but not in a monetary sense.
However much it has cost me monetarily, it has repaid itself ten times over in value to my very soul.
I (probably not obviously) meant the "too much" part, where kids fail to grow/launch when (optional) things are given to them too easily.
I didn't mean food, shelter, medical, education, lego, others-where-required-by-law.
I can't imagine spending $48K on a home GPU server, but I did just splurge and buy a PC with an RTX 5090, specifically to hold the largest models you can fit in 32GB. It's a top of the line PC with water cooled high end CPUs, 64GB RAM, RTX 5090 for $5K. To me the jury is still out whether this was a worthwhile investment, but I do expect to use this machine for a decade. I don't run it at 100% power (it's mostly idle, except for times when I'm training or doing batch inference). It has the nice property of being blackwell generation, similar to the machines we use at work.
It just scares me to own a box that is $48K in my house, especially if it breaks, or gets stolen.
I was looking at Ultras for sale, and had same worry, so didn't end up getting one. I have some peace of mind comfort about applecare and technical repair, but i couldn't find insurance that would cover theft (or rather, i did, but it was too expensive)
>> To me the jury is still out whether this was a worthwhile investment, but I do expect to use this machine for a decade.
The high cost and power consumption are both signs of the death of Moore's law, so you are probably correct that this system will be near state of the art for some time.
Yes! It scared me too. I tried to insure it under my renter's insurance policy, but they not surprisingly refused. I had to get business insurance to cover it
Not even a single mention of gaming.
No wonder gamers hate AI bros.
I have a second computer with an RTX 4090 for gaming (running Windows). I also used the new RTX 5090 running Linux to evaluate whether Proton/Wine allow me to run Windows games on linux (yes, it works, but the compatibility and frame rate issues make me stick to native Windows for now).
If you want a GPU that has comparable performance on Linux to Windows- you want AMD. NVIDIA drivers are notoriously bad. Many of my games run better on Linux with the open source AMd drivers. (CachyOS rolling rolling rolling).
Sadly if you want a GPU with good AI performance you gotta go with NVIDIA. It might sound crazy but as a 7900XTX owner.. My 12GB 3060 on my linux server outperformed the 7900XTX by 40%. The 3060 only has half the vram of the AMD card. Proprietary drivers under Arch Linux.
On top of the significantly worse software on AMD's side (literally didn't work on windows in particular - so the "performs as good on both systems" is a nonstarter, some GGUF library dependency just doesn't work/exist under AMD on windows). Had me running the AMD card on windows under WSL (not a problem with nvidia though, that ran just fine on windows-side directly).
Aaaand also the other AMD bugs, such as the pink squares display corruption that has been an active issue for my GPU in particular (7900XTX) for over a year, maybe approaching two at this point, with no fix in sight from the AMD team (barely and ack at all - not on a single patch notes, just a bunch of reddit discussion). Really regret spending so much on an AMD gpu.
Network effect for graphics cards? Literally what? Your friends don’t care what GPU you run my guy and there is not much benefit of having brand loyalty to a company like Nvidia that gives absolutely zero fucks about people that aren’t their enterprise customers buying GPUs by the thousands. If there’s any “network effect” for gaming GPUs on Linux it’s in favor of AMD because of the immense amount of work Valve has been putting in to make it work well for their steam* hardware.
Nvidia’s drivers are trash for gaming on Linux and the majority of your “compatibility and framerate issues” are because you’re using a sub-par product for the job.
I wonder what's going wrong there? Personally I found compatibility and performance on Linux to be extremely good. And just keeps getting better. And that's not even just me, that's all kinds of benchmarks out there. Sorry to hear that. : ' (
No idea. I agree that in principle I should have close to the same performance on Linux. I just didn't want to spend a bunch of time customizing configs and updating software so I could reach parity with Windows when I had two computers.
I have the same rig as you minus watercooling, and I assume you have AMD Ryzen 7 9800X3D? Anyway, it's my only PC now, I game, dev, run local models, edit photos, edit videos, all in Manjaro. I get ~70FPS in Cyberpunk at 4k, every setting at "Insane" or whatever goofy thing they call it, Ray tracing on path tracing off, with no framegen but with DLSS set to quality. Without DLSS I get around 40fps. Seems equivalent to what I see online with people with a similar build on Windows.
I run hyprland, seems to be the only wayland based keyboard-forward WM that has good nvidia support (and, allegedly, supports HDR, though I haven't got this working). I heard gnome was pretty good otherwise. I was running i3 before and it also worked fine, however once I got into wanting to get streaming working, there wasn't good compatibility between i3/xorg and tools like sunshine. I believe steam streaming worked fine on it though iirc.
The only thing I miss from windows: easy streaming with sunshine/moonlight. Steam streaming works (usually heh) but it took me a couple days of fiddling to get a stream to work at all through sunshine, and it is choppy. But for local gaming, I don't miss windows at all, I'm so glad to finally have all my drives converted from NTFS to ext4.
No, it's an Alienware R51 with Intel Core Ultra 9 285K 3.2GHz Processor; NVIDIA GeForce RTX 5090 32GB GDDR7; 64GB DDR5-6400 RAM; 4TB Solid State Drive; Microsoft Windows 11 Home; 2.5GbE LAN; 2x2 Intel Killer WiFi 7 BE1750+Bluetooth 5.4; Liquid Cooler
I don't see it on the Dell site anymore, only more expensive, lesser configurations (good timing on my part?).
Yeah, I really want to put in the time to try out various games, but realistically, the whole point of getting a second computer and installing Linux was to be able to train and serve models, and switching between serving a model (that people in my house want to use at random times) and gaming didn't seem like a great choice. If I did get good results, I'd seriously consider wiping Windows 11 from my older machine (an older Alienware with a 4090), but to be honest, I'm perfectly comfortable on Windows desktop.
Having built an almost identical rig earlier this year can promise at least one similarly-spec'd machine gets equal use between AI and gaming (Both on Linux). Stupid-excited for the Steam Frame to finally come out.
or crypto... what's old is new again.
I would probably hate someone if they were buying the same hardware as me but doing something actually useful with it. Any game worth playing doesn't require high specs anyway. There is such a large catalog of old games.
I specifically got the previous model so I could play AAA games with all the settings set to Ultra, at 4K. Cyberpunk 2077 struggled even with my 4090, so I had to disable ray tracing and enable DLSS. Since I've run out of new AAA games I've been playing older ones and it's crazy how fast they are.
> No wonder gamers hate AI bros.
Personally, playing with AI models is way more fun than getting sucked into a game loop. Game loops feel like busy work hooked to an engineered dopamine drip. AI models are new frontiers and are exciting to build with, modify, lobotomize, and hack around with.
And some of us are doing AI stuff all freaking day at day job and just want to play some Tekken when we get home for 30 minutes after the kid is in bed. But now Playstations are 1000$ and Ram and GPU prices are astronomical.
Not everyone is hustling 24/7 like some kind of lunatic.
[dead]
I remember playing Quake III which had user-programmable bots and thinking "wow, this is a really hard computer vision and reasoning problem". And then realizing "huh, that's a major research area, I should work on that". Later I learned that the bots were fairly simple and worked on far simpler world representations (nav meshes).
It looks like DM took a crack at it: https://deepmind.google/blog/capture-the-flag-the-emergence-...
'If you google “plugging a PC into multiple outlets”, you get lots of warnings that if you even consider such a setup you will instantly burst into flames. So I hired a professional PC builder make sure it was safe.'
Not really sure how that makes it safe but OK!
I guess it was supposed to be a humorous aside, but it wasn't actually helpful because the relevant issue is when you pull more total amps from a single circuit than it's fused for (usually 15 or 20 amps in U.S. residences). The failure mode is usually tripping the circuit breaker.
That issue can often be addressed fairly easily by splitting the power draw between two adjacent circuits. You can have an electrician do it permanently or temporarily DIY it with an appropriately rated extension cord. The real issue was OP was in an apartment at the time so an electrician would have been difficult. I assume they decided to just have a system integrator build it because they didn't want to figure out how to segment and route the power rails in a dual power supply system, but it's not exactly rocket science. Problems are often more due to choosing power supplies that aren't up to their claimed spec, not pre-testing them under load or using incorrect or under-spec cables.
I think the relevant issue is you could conceivably have a house with two outlets with opposite phases. Bussing them together in the PSU will then create a short
> two outlets with opposite phases
This is actually THE standard in the US, which is actually fundamentally a 240V power grid but with an electrode stuck halfway down the secondary winding on every pole transformer, which becomes your "neutral". The two ends become L1 and L2, so that L1-N is 120Vrms, L2-N is 120Vrms, and L1-L2 is 240Vrms, and this is what goes into every home.
The power outlets connected to L1 are all opposite phase to all the ones connected to L2.
Rather than bussing the two outlets together, what you can safely do is get an electrician to just wire up an outlet with L1 and L2 and voila you have a 240V outlet. This is how you get all your dryer outlets, EV charging outlets, electric stove outlets, etc.
Probably means hiring someone who has more knowledge about PSUs and especially about having two simultaneous PSUs. There are questions like: when you press the power button how do the two PSUs turn up and in what sequence? How do you deal with the PWR_OK signal? What if there are voltage differences between the two PSUs? What about power backfeeding?
I read this as; the "professional PC builder" would carry some sort of insurance. So it isn't really "safer", but if something goes wrong, the investment is (potentially) still safe.
Just an assumption, though!
Hi! Thank you so much for posting this! I got back luck/timing when I tried, so happy it made it to the front page! (I am the author)
I did this with used parts and cheaper consumer cards (3090s) and did much of the same calculations. I found it was way cheaper for me as well.
The main advantage, however, is that the friction of "this is going to cost me in tokens to even try" goes away. I was so much more willing to take chances and try new things on my own hardware than I would have been if I were paying API costs. I feel like this point isn't made clearly enough by those of us who run these absurd self-hosted inference systems.
Thanks for the write up, was a fun read. I spent an order of magnitude less, but I could relate to your story from beginning to end.
Epyc (Milan), 512gb ram, 4x 3090
You kind of bury the lede in that Article, it's a good article, well done getting interest in your work.
Will you now be selling these GPUs for a profit?
The other advantage of the local GPU is that you are not feeding your data into cloud providers. I'm not sure how much you can really trust Anthropic and OpenAI not be improving their models based on your input.
Doesn't it benefit me if the models I use improve?
Do you value the infinitesimal improvement of model quality more than your privacy?
Great article. I'm about to embark on a similar journey.... Doing a ton of AI development right now. Don't need a server, but a very, very high end workstation is super appealing to me right now. Looking at $50-$80k. 1TB RAM. 2x RTX Pro 6000s. 64 core Threadripper Pro. As many 4tb or 8tb nvme drives as I can stuff.
I envision NixOS at the core... then everything I need virtualized on top with KVM/QEMU. Maybe a dual boot setup with Windows for gaming and Flight Simulator (but I could virtualize that too with easy GPU passthrough.)
Lingering questions I'm working to figure out:
- Will 2 RTX Pro 6000s run on a 1600 watt PSU? Not sure how much higher I can go without calling an electrician. (standard US home.)
- Assuming I plop this into my home office, should I expect the PC to run significantly hotter than my current rig? (3960x threadripper, 128GB RAM, 1600watt psu, overclocked and watercooled 4090.) My water temp, measured at radiator, is about 60c at peak load. (This is the only number I care about, as this is what I have to consider to be comfortable sitting next to it.)
This is a difficult calculation to make because you wouldn't rent time on the exact same system in the cloud. Depending on what you're running, a bigger server with better inter-GPU interconnects in the cloud might complete the task so much faster that the additional per-hour expense is more than covered.
Right, you can rent from a v100 from llama cloud for $0.79/hr. An h100 is $3.99 /hr.
$48000 is equal to 12000 hours of renting an h100, which is about as long as you’d spend at your job for 6 years!
Agreed. And the gained time either goes toward 1) more experiments, or 2) leisure, which makes you sharper in the lab and happier overall. Not sure the "I saved $17,000 so far" framing is the most useful way to look at it, but it's a cool project and I love that people are doing this kind of thing.
FYI: If you're in a similar situation, think very carefully before you build your own. The $17000 might sound like a lot; but when you take into account your time and risk tolerance, renting might be a much better solution.
I think their retrospective at the end of the article is grounded and logical:
"If I were to do this again, I wouldn’t do a custom build like this. I would buy a standard datacenter server and rent space in a colocation center"
I'm sure there are use cases when renting makes sense, but it can get crazy expensive really fast if you're not careful.
Just curious OP (if you're the one posting) -- what do you mean by independent researcher? What are you researching and are you making $$ from it or are you living off previous built up savings? Seems like an interesting path. What research have you looked into so far?
They have a subsequent post (from Monday) about what they've been working on: https://rosmine.ai/2026/05/18/fixing-llm-writing-with-distri...
(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.)
Shallow take: They made an LLM that uses fewer emdashes.
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!
Or they just have lots of money and a hobby. Someone else might blow $48K to get an old Cessna and go have fun flying around. Not everything needs to have a purpose.
You read that line wrong.
You were on the money with the Cynical take lol:
https://rosmine.ai/2026/05/18/fixing-llm-writing-with-distri...
Huh? That's them intentionally demonstrating the slop style.
I am not the author, but he has been training/tuning? a model that produces text that mimics the source material in a more natural way. So getting the LLMs to produce less bland and boring LLMisms, according to the following up blog post.
citing from the article:
"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?
Stuff like this + OpenClaw with Mac Minis a while back is sort of exposing a probable local AI flywheel waiting to happen.
Someone needs to solve proper distribution of packaged GPUs with some Tesla-like wall connector for a consumer grade box that is plug and play.
Maybe John Ternus ends up doing that at Apple since they sit closer to this consumer profile.
So the answer is: "TBD if I can actually make money to pay this back"
If nothing else, rosmine's DFT [1], which is what they were working on with this setup, seems like a worthwhile investigation.
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...
Giving an LLM samples and tell it to apply the style in the sample works a lot better than just telling it to copy a style it may have seen, or a list of rules.
They do it well enough that it'd take really good output to beat.
They really don't.
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.
I have seen examples that shows otherwise, including from a client that tested it extensively by paying people who thought they were paid to help detect AI generated content. They did little more than what I described. It works very well. Some people still insist they are able to tell the difference, but in the tests I saw, people did little better than random chance.
Some of it you could probably tell with statistical analysis, but actualy people are far worse at judging whether content is AI generated than they think they are.
If you need to beat an AI testing tool, you need to do marginally more work than to stop people from recognising it, but not all that much.
The nature of it is that you don't "see" most of the stuff that is well done because few people want to talk about it.
From the author’s POV it seems like they were going to do this research regardless, so this is asking what the most cost-effective way to do that research.
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.
(For reference I’m talking about the DFT post from the same blog.) I love that ML is still in the “gentleman researcher” stage where relatively small amounts of startup capital can buy a ticket into frontier research.
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...
I agree :) Also, I heard Teknium trained the original Hermes model on 2x 4090. You can do a lot with a little compute
The $48K also isn't fully sunk cost - there's a non-trivial residual value for those GPUs at the moment and likely for a few years yet. The server has a depreciation curve that's pretty enviable, actually!
And here I felt like I was wasting money on an Intel B70 to run LLMs locally.
Jensen Huang said 'the more you buy, the more you save,' and you actually took it personally.
The idea is similar to maintaining on-prem vs cloud
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)
Cloud servers have cheaper electricity, the scale of industrial-level cooling, no issues for you (as a user) with hardware failure (ie you just use a different server; it's not your problem) and can amortize their cost by running 24x7. I've seen H100 computer hours for as little as $2.
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).
I suspect a standard 20A 110V circuit can probably handle 2x RTX 6000 Pros. 15A probably can but that requires more research.
During initial setup of the server I am putting together, I found that a machine with 4x Blackwell cards derated to 300W can get by on a single 120V 20A circuit. It's tight but doable. A lot depends on the power supply. I don't think it's a great idea to run 4 high-power GPUs on a single ATX-style PSU, even a beefy 1600W job.
The other questionable part is whether all four cards can temporarily spike at full power during boot, before the wattage limit is applied by the OS. Some accounts say this is possible, and if so it could shut down the party in a hurry. But I didn't see any misbehavior when I tried it.
My earlier research suggests NVIDIA does not actually cap spikes, it caps the average over short periods of time. So setting the power limit is no guarantee.
I have four old 24gb Nvidia cards. They're not great but they're not useless either. The problem is that I haven't really figured out a good way to actually use them.
Genuine question; would anyone here recommend any specific motherboard to best utilize these cards?
Depends what you want to do and which cards you have, but usually going with any older (3rd gen+) threadripper pro setup will give you a lot of pcie lanes.
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
You could ask AI and get pretty far reading the answer.
I know. But this is a forum filled with technical professionals and I would like to get actual opinions from actual humans.
AI is cool but it's not going to have all the good and bad experiences that humans have had with different motherboards.
Actually that's the best part of AI. It has access to experience with way more than the select sample size here.
I'm not entirely sure what your point is here; me asking for humans to give an opinion does not preclude me from also asking AI.
I'll buy it from you!
He didn't consider the possibility of renting it out during the downtime to Vast.ai to make some money back.
So some things have changed since this rig was first built (2024). The most relevant is that $6800 RTX 6000 Ada 48GB has arguably been supplanted by the $9500 RTX 6000 Pro 96GB.
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 am also considering to buy 3-4x RTX 6000 Pro 96GB plus some Ryzen workstation with a grant.
Is this the best general-purpose choice as of 2026 with $50k for training, fine-tuning and running large open models?
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I have the Macbook M5 MAX with 128 GB of RAM. I put its performance at roughly equivalent to the RTX 5070 Ti. The M3 Ultra 512 GB for me is about half the performance of the RTX 5070 Ti but obviously it has the ability to do more because of the increased memory.
I don't think anything compares to the nVidia chips at all.
You would install a 240v circuit (in the US) like for an electric clothes dryer.
Edit: I now see the author was in an apartment and couldn't do this, so I concede this is not responsive here.
Quick tip for people who want to experiment with local models: A lot of the common smaller models are also available on openrouter or other services. Dirt cheap.
I know it's not the same. But a lot of people buy expensive GPUs, just to find out they have no real use for smaller models.
You guys are nuts... I hope you're making enough money to justify this level of investment and power use (not to mention noise and heat management) in your home...
I'm just putting a 2nd hand 12gb 3060 into my lab box, but its only for use with HA/Paperless/Plex etc type things. I dont need multi-model agentic behavior for private use.
If I did I reckon I'd renting infrastructure rather than filling my home with that sort of gear.
> Because of this I got a motherboard with slow GPU interconnect. It’s good for running many small experiments in parallel (which is my main use case) but horrible for any models split across gpus.
:( you paid a professional pc builder and you weren't told this?
> paid a professional pc builder
They did not. That's a mining rig not a workstation. It's visible from the photo and the chart showing multiple failures over a short period of time including the risers -- which are visibly very low quality -- failing twice.
You have 50K, you call a real expert like Puget Systems or Digital Storm.
I wonder why using 2 PSUs resulted in having slower interconnect.
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
If you split models using pipeline/layer parallelism you don't have to care about a slow interconnect, you're just slowed down a lot when running a single inference at a time as opposed to a fully pipelined minibatch. But tensor parallelism requires much faster interconnects than you could get in your average server, so I'm not sure that a different motherboard would help all that much.
what is a "professional pc builder" in 2026
A guy on Facebook with more confidence and better insurance
Consumer motherboards can still make sense even if you leave some performance on the table. Running an actual 8x GPU server is not something you'd want to do in an apartment. Imagine the old Lucasfilm "THX" trailer where an unearthly-sounding foghorn whine rises to a sweeping crescendo at reference level, only without the decay at the end.
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.
Did you think about Max-Q cards? 300W and they aren't that noisy either, 14% lower perf than non-Max-Q card.
That was an option, but having decided on a true server chassis for other reasons, it made sense to use server-edition cards to take advantage of all those fans. I downclock them to 300W anyway for longevity, but it's nice to have the option to go to 600W if needed.
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.
Don't those Ada 6000 GPUs support NVLink? I think I can even see the cover for the connectors in OP's pic.
edit: Hm, finding mixed information online on whether that's still supported or not. Apparently it was removed in workstation GPUs.
Nope, they don't support it. And afair even if they did, you would be limited to connecting only in pairs, not all 6 together
Honestly, I made the same mistake when I added a GPU to my (not $48K) existing homelab. I got a Ada 4000 for its slim form factor and low wattage, but realize after I bought it that it does not support NVLink, so I can't really effectively double it up later if I wanted to. Live and learn. I suppose you might research that a little before blowing that much money though LOL :)
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The research that's presented in another article on the same site is way more interesting than the betteridges law article linked here. It'll be very useful in my own latest project if this research is incorporated into some model I can rent by the token!
no
out of curiosity, did you check how much would cost to rent a cage in a colocation space? Having to power your computer from two different outlets sounds wild..
the very last line of the article:
"If I were to do this again, I wouldn’t do a custom build like this. I would buy a standard datacenter server and rent space in a colocation center. But then I would miss saying Hi to grumbl once in a while."
Yes, i mean, he could rent a cage and run grumbl it there. It doesn't have to be a standard datacenter server, even though a standard datacenter server would be better and cheaper.
A cage[0] is ~100x larger than what you need to host a single server. Many data centers will colocate by the rack unit. At others you can get a quarter or half cabinet[1]. Even at the very largest enterprise datacenters you can colocate a single cabinet.
[0]: https://static.cisco-eagle.com/images/category/WireCrafters/...
[1]: https://www.edpeurope.com/wp-content/uploads/EDP-3-Compartme...
The cheaper, easier solution would probably be just to get an electrician to wire up a high amperage 240V outlet just like your electric stove or dryer has, and then get a PSU that connects to that.
Would probably cost you $500-1000 depending on how difficult your home is.
It doesn't cover risk. If one or more gpus dies, who pays for it? If you rent, you are guaranteed to be insulated from this risk. But owning, you might not have the best return policy from the vendor. And if you are actually at fault for breaking it, they have every right to deny a return. Or if your apartment is burglarized or catches fire (possibly from overloading the circuit) you are out the entire investment.
Also a lightning strike or surge from the electric utility could fry the whole rig. Proper protection costs thousands, and even then it's not guaranteed to protect everything
> Proper protection costs thousands
Frankly that's something a landlord should provide. And there's insurance against losses from electrical issues.
Why should a domestic landlord provide you with data center-level power protection instead of just the normal household utility connection?
To protect a large electrical device investment, you would want an EMP shield whole-home SPD, in addition to an SPD right at the electrical device. The first one shields exterior surges (including non-terrestrial), but the second shields against internal surges. And yeah lightning will blast through both of them. So the best bet is probably a lightning strike detector combined with renters insurance.
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> I thought that I could not get a standard datacenter server because my apartment wouldn’t let me upgrade the circuits, so I needed to have 2 power supplies plugged into different circuits.
Why didn't they just put a higher amp breaker in the box?
It is unsafe for wires to be handling higher power than it was rated cause the wires act like very low ohm resistors. At some high enough I, you’re still gonna be generating power P=I^2R which is mainly thermal and melt the wires.
This is, sadly, obvious and inevitable in retrospect.
The two major drivers of inference costs are GPUs and electricity. You can't get cheaper GPUs, but you can make existing GPUs not sit idle, and you do that by utilizing them 24/7, processing user B's request when user A is thinking, and handling many requests in parallel, neither of which you can do as an individual. You can get cheaper electricity... by moving, and it's much easier to move your AI workload than to move yourself.
This is a completely different dynamic than renting houses or apartments, as you can't really rent out the same house to different people at different times of day.
Yea. LLM inference requires batch processing to have a shred of hope at being cost efficient. Batch processing requires a not so insignificant amount of scale (but probably not as much as people think).
I'm very pro local models, but not to have parity with SoTA frontier models. Just contextually trained small models doing smaller specific tasks.
Trying to run bigger LLMs for an individual user to do big tasks is not going to be a good time.
Historically it was not uncommon for beds to be rented out to multiple people.
I’m not usually one to ask this because learning to do a thing can be fun, but why exactly have you spent 25 thousand dollars on getting an LLM someone else made to answer maths exam questions?
I didn't spend that much, only $6500 AUD for a GB10 based Asus GX10 which is even slower than OPs, but I spent that because it makes for a great learning platform. Theres not much else that lets me fiddle with 128GB of RAM for my graphics processor, and it's quite lovely to be able to run things as long as I like without worrying about my cloud instance being shut down.
It's not financially a good idea: renting really does beat owning, and cloud beats both if you're only running inference on these machines. But I'm not just doing inference, and as a thing I can do silly stuff on to learn, it's hard to beat!
You could just rent a bare metal server with those specs
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$6500 AUD can get you a good chunk of B200 time on any of the GPU neoclouds :)
When you say you are not just doing inference, you mean you are also training your own llms? I am curious what other things can be done.
The cost is obviously not that big of factor for OP as it might be for others. It's actually refreshing to hear the candid viewpoint that he expresses here.
25k is definitely a lot but I did the risk analysis and I figured worst case I would lose a 1000-2000 after a year of playing around with it, so I look at it more like renting (I'm going to keep the Macbook Pro no matter what since I needed a new one).
Either I don't understand the used apple market.. or I agree this is crazy. Someone spends $25k on new hardware, waits a year, and expects to sell it for $23k? Unless the ram issues save him, and cost of new goes up, I don't see how that was going to work.
> I don’t think -$2000 is a conservative enough figure for standard depreciation either (a lot can happen in a year)
We aren't exactly in "standard" times and haven't been for quite a while. Even five year old graphics cards are worth more today than they were just a year ago. Things will obviously depreciate at some point, but you gotta throw your existing notions of how quickly and how much hardware will depreciate out the window. There's just been too much money dumped into AI for a "well I guess this won't ever pan out, let's dump all this hardware to recoup our costs" moment to happen and tank the price of everything suddenly IMO.
And that's not even getting into the other geopolitical stuff going on right now. Strange times.
Aren't things like this seeing 'negative depreciation' these days?
Sure, they took a gamble that they wouldn't be able to sell it used.
If you are able to tie up $25k for a few years just for shiggles, you clearly are able to make do fine without that money and if lost it would be at worst annoying, not catastrophic.
I assume he is calculating the loss as depreciation - what they would have spent on cloud bills if they hadn’t been doing this locally.
I mean whatever. It's workstation/server class hardware, that's how much it's been for a long time
I think op would make a really good pope too.
https://news.ycombinator.com/item?id=48118672
One year ago finetuned local LLMs had a significant edge over ChatGPT or Claude. Look up in YouTube all the DIY videos testing LLMs on their own machines with different setups.
Remember: one year showed up to be a gigantic leap in regards to quality of results and innovation in the AI space. Agents weren't really a thing and vibe coding wasn't even invented as a term because the top notch tools at the time were lousy, with lovable being the frontrunner with its - in my view - sorry Tailwind recombination tool shaming AI to do the work.
Then fall hit 2025 hit us, new year's eve and suddenly there was such a massive surge of innovation and competition with ChatGPT Codex suddenly showing up.
Remember: one year ago many now commonly used tools weren't yet available like Nano Banana or Codex.
"The 25k are so vast" - Yes, and no. For example, if the machine is bought for business usage I can deduct the costs from taxes. This roughly amount for 50% of the financial burden.
So I jokingly use to say, that I pay only half the price for my Apple business machines. And yes, I am strict in this regard. Business means business. No private emails etc. nothing on my company computers.
Maybe there are other options as well to reduce the financial expenses the dude mentions, but it doesn't seem so.
I would also go for leasing, this way already the monthly payments can be deduced and I don't need to buy and maybe resell the machine.
Apple is a luxury good. Without business usage or at least partly using it for business as well as private (mixed usage in tax reports) I wouldn't buy the devices or think twice.
Apple under Cook evolved into a Gucci like luxury brand, that is more and more a rip off than quality delivered, especially considering the latest OS updates for Mac, iOS and iPad. Apple is a mess, following Microsoft Windows' footsteps happily, because the CEO is as has been correctly assessed, no product guy.
But I stop with my rant here.
Always try to use tax deduction as leverage for your computer expenses. Every citizen should invest in basic knowledge about that.
Even a 10-20% professional usage for work (mixed usage) gives you a noticeable advantage over normal pay.
Privacy and offline operation are valuable or non-negotiable in some cases, but the difference is pretty categorical between what can run on a single card and what can run on a DGX GB200 NVL72 cabinet. Doesn't mean it's not worth seeing how far local models can be pushed. Not every problem needs a senior engineer.
I know it's one of those "if you have to ask" situations, but curiosity got the better part of me. Here's the search assist response:
"The DGX GB200 NVL72 AI server costs approximately $3 million per unit. This system includes 72 Blackwell GPUs and 36 Grace CPUs, making it one of the most powerful AI servers available."
The search assist actually credited a source used with: https://www.tweaktown.com/news/98292/nvidias-new-gb200-super...
That $25k spend by GGGP seems like nothing in comparison. That's ~1/3 of one chip in that cabinet. God gawd I'm old and out of touch with modern AI data centers.
It's The Circle of Computing Life. The pendulum swings between centralised mainframe timesharing-for-hire and desktop individuality.
We've been in a centralised phase for longer than usual - first cloud everything, then AI - but at some point in the next decade prices will crash and a market will appear for personal, local intelligence.
> the difference is pretty categorical between what can run on a single card and what can run on a DGX GB200 NVL72 cabinet.
A better way of putting it is that you can run plenty of things on a single ordinary system, but you may be disappointed at the performance. Generally, you can't expect inference to be as quick as with cloud for SOTA-like models. You have to run smaller models for quick replies, and large models with a lot of real-world knowledge for less time-critical inference, possibly batching many requests simultaneously to improve throughput.
It's just a project I'm working on. I'm working on projects where AIs are processing and classifying large amounts of data that would be a lot of work for humans to do.
I think of LLMs as being well equipped for handling dynamic data or adapting to unforeseen circumstances well (random code requests, website's ever changing layouts, typos, non-standard formatting in docs, groking out important info, etc), but math problems are be definition a very specific set of instructions to run, so is the overhead and "thinking" aspect of a LLM/AI even needed here? I'm genuinely curious, btw, I'm not asking sarcastically. Can't these math problems just be yanked from some test file and rapid fired directly at a gpu/compute unit?
Nono, parent was asking “They’re bad and inefficient at that, so why have an LLM do math? Why not just use some code and the CPU/GPU that’s already good and efficient at basic math?”
Because buying Macs is not about performance, its about feeling like you are rich.
That money could have been spent on way more bang/buck performance in the form of a set of 4 graphics cards.
Also I would probably put the odds 70:30 that Apple marketing is astroturfing on HN from the amount of posts about running llms on Macbooks, because in reality, the inference speed of any decent llm is unusable on a Macbook despite the ability to fit it into RAM.
Or it could have had way more bang/buck by feeding a family of real brains for a year or two
That hardware is costing him ~1$/hour over 3 years. Presumably having it answer math questions was a tiny fraction of what he was using it for.
I’ve spent twice that on hosting movies and tv for Plex, so… I think they are worthy of my praise. What a healthy outlet for money.
You spent 50k for plex hosting? Why so expensive?
That’s a lot of blurays…
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I got an RTX 6000 pro too. I like running locally, I've learned a lot more than if I had used an API and there's less worry about overspending tokens. I accidentally spent $100 on claude api in like 2 days because I didn't know what I was doing.
The problem is that while one these gpus is a huge improvement over a laptop or a single 3090, you very quickly wish you had more. I would buy a second one, but I did the math and realized that with the current crop of models, 2 Blackwells doesn't buy me any new capability that I didn't have with one. So I would need a 3rd one. And when I buy a 3rd one I will feel like I want to running a higher quant, so then I will want a 4th.
You can fit Deepseek 4 Flash on two with TP 2 and 6 different streams at 65k context. 150 tok/s
A pair of RTX6000 cards will give you a good performance boost due to tensor parallelism, though. I haven't tried the newest predictive quants but I see about 35 tps when running the 8-bit Qwen 3.6 27B model on one board and about 50 tps on two. Probably could come close to 100 tps on an optimized setup with the latest GGUFs.
Also, the 4-bit quants of MiniMax 2.7 will run at 100 tps or so with two cards, which is pretty decent. It doesn't go any faster at all with 4 GPUs from what I've seen, so if you don't actively need 384 GB of VRAM, 2x RTX6000 is a good place to be.
You can get 70-80 tps on qwen3.6-27b f16 with MTP on a single card
>> find a reliable place to sell it without getting ripped off by scammers.
This is a real problem and why I've just about given up on ebay or fb marketplace, esp for computers. If you are in Canada though sellit9.com is a great solution to having to deal with sketchy buyers.
I have three m3 512gb units and want a fourth to run an exo set up. Like you, I am worried about scammers. Let’s discuss if you still want to sell.
https://calendly.com/ryanwmartin/open-office-hours
If you're in a decent sized city, you should be able to find a local buyer on Craigslist or FB Marketplace... Beyond that, for higher value, smaller items like your M3 Ultra, I would talk to your local police department and/or library to see if you can do the exchange there. Larger libraries usually have a police officer on site or nearby, and the PD office near you may also provide a "safe" exchange location... I'd bring a monitor/keyboard/mouse so you can demonstrate the system working properly.
YMMV but between your nearest PD office and Library, you should be able to use one or the other for your exchange of goods/money. The biggest thing I've sold is a mid-range video card during late covid (I managed to get a better one via newegg shuffle) so I sold the old one (RX 5700XT -> RTX 2080) to make up the difference a bit. I just did the exchange at the Starbucks near me for that.
Something is very wrong in some countries if you have to get police protection to sell a f* computer. I get it’s on the expensive side but still….
See e.g. https://www.murphytx.org/843/Safe-Exchange https://www.ottawapolice.ca/en/community-safety-and-crime-pr...
Police "safe trade zones" are basically a parking space outside a police station, with a sign.
You don't have to... but it's a matter of a safe location for both parties. If it was more expensive, I'd probably work through a broker (like a car or house).
The buyer doesn't know who the seller is, and vice-versa... the level of trust you can bear depends on how much you're willing to lose. My advice is only in that there are safe venues you can use to make such an exchange.
Not really. Every country has a nonzero number of criminals. It's entirely a matter of the risk/reward tradeoff. A small consumer item over $10k is well into dangerous territory.
Are we talking about a cash transaction? If so >$10k is dangerous as the police may want to steal it themselves.
If it is an electronic payment, I'm not sure how completing the transaction in front of a police station will help any. Well, it will help the buyer to see it working, but the seller gets no additional protection besides seeing "a person."
Better sell it fast before the M5 ones come out.
Which of these has been the most productive for you? Sounds like you've enjoyed the RTX6000 the most?
RTX 6000 is some-what obviously my fastest card but my biggest problem with the RT 6000 is the immense heat. The GPU itself is almost 200F and the exhaust from the fans itself is over 150F. I'm worried that my hard drives are going to fail. I was told that the GDDR7 is even hotter than the GPU which is surprising to me.
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.
That's about what my OC'd and watercooled 4090 runs at. The cards are designed for it. Only problem I have is when sitting next to the computer under load -- I either have to open windows or blast the AC. Too bad I don't live in a cold climate -- that 60c heat output would come in handy :)
> Too bad I don't live in a cold climate -- that 60c heat output would come in handy :)
Used to overclock back in the day during winter with an intake duct rigged to suck in outside air, best thing about -30c :)
From my failed and expensive affair with GPU mining 5 years ago, You can get a great heat dissipation outcome by using an open case with a lot of directed fans at the expense of a bit of dust and lots of noise
Since you are not running realtime 3d grafix, could you put the card in an external chassis so the heat is not in the same box as the SSDs?
I take it this wasn't the half-wattage Max Q version with blower fan?
Given that the tokens are being subsidised by a couple orders of magnitude, would it still be as cost effective long term?
All of these have appreciated in value. How much are you looking for the Ultra?
I've seen a lot of sales on eBay for over $20k, but I don't know if I believe it. Plus the lack of seller protection and the prevalence of scams on eBay make me too hesitant to actually want to risk it so I don't know what to do haha
Haha, yeah, it's about $23k or so. Should be twice the price what you bought it for if you got it last year. Tbh I don't know why. The RAM is large but the bandwidth and the compute isn't nearly enough. You can fit DeepSeek V3 on it quantized but inference is like 10 tok/s. Honestly, you'll be able to sell it locally for that in cash, and I would in your place.
I saw your heat comments about the RTX 6000 Pro as well. I bought a few of them recently and I'm running 2 of them in a 2U case in a colo. You need a lot of active airflow to keep them cool. Mine range from 23 C to 80 C.
Well if it makes you feel better those frontier LLMs are all technically taking a big loss, and they may all be in your shoes after a few years.
> I figured worst case scenario I can sell them in the next year and only take a haircut as opposed to losing my entire investment.
It's going to be a non-trivial haircut. This stuff depreciates pretty fast.
Bizarrely, I brought a GPU new in Jun 2024, and there are sold ebay listings saying the used GPU is worth 4% more today.
Of course, this is an unusual state of affairs; I see my GPU purchase as consumption, not investment.
You definitely want to get rid of your M3 Ultra before the M5 Ultra get officially announced.
You'll probably make a profit by selling them today. I bought a M1 Max Studio with 64 GB last year off FB Marketplace for $1000 and today I'm seeing numerous 32 GB M1 Maxes for $1200-1500.
Yes the prices on eBay for the Mac Studio are all over the place, but I've seen sales for over $20k. I don't know if I believe it but there's enough to make me think if I can sell it for that price it would be worth it, but eBay has basically no seller protection so I'm not willing to take that chance.
Running LLMs on Macs is still terribly slow. They simply lack the optimizations other platforms have.
An RTX 6000 pro Blackwell is a pretty good card
A M3 ultra mac Studio can run models that do not fit in similarly priced computers with multiple Nvidia GPUs. And it will use a lot less electricity while still having good enough performance. Except the pre-filing perfs that are quite poor on the M3.
M5 pro 48GB should be good and future proof
If you buy Mac get at least 256GB ram otherwise just buy a bunch of nvidia cards. It really does not make sense otherwise if you are looking for performance / $. The mac (studio) is unique as it has more ram than the alternatives(I.e consumer nvidia cards or spark stuff) so it can fit bigger models but otherwise its performance is worse.
If you are in the bay area, i'm happy to buy that M3 Ultra from you, i've been unsuccessfully looking for one and can't find any.
No harm in listing it for $20k, and if it sells, that's an easy $5-10k for you.
I'd buy that Mac studio m3
I'll buy your macbook if you're trying to get rid of it!
I'm keeping that one for sure, I love it!
How do you use the RTX 6000 with the Macs? Exo? I would think that would be pretty snappy if configured properly.
This is on a separate Windows PC, I don't have it integrated with the Macs.
If you don't need cash right away, I'd wait until the M5 Ultra comes out and see how things shape up. There have been some early efforts aimed at combining the prefill performance of a GPU with the high throughput achievable with the Mac's unified memory architecture (see various YouTube videos by Ziskind and others, as well as https://old.reddit.com/r/LocalLLM/comments/1r6drpi/exo_clust... ).
Point being, once the M5 Ultra is available, I suspect a lot of people will get very serious about making Macs work with RTX GPUs because that will yield an inference platform with a good bang:buck ratio. If so, you may find that your existing hardware is more powerful than it seems today. And it may be a lot more expensive to replace later if you sell it now.
I'm not really asking this from the perspective of whether I should buy hardware. I'm trying to understand the economics.
The AI space is moving so fast that it is hard to know which conclusions are stable. After all the discussion around local models, is the practical conclusion still that API/frontier providers have a huge structural advantage because of datacenter hardware, high utilization, batching, optimized inference stacks, and perhaps strategic pricing?
In a comparison like this, a $25k local setup versus buying tokens, what multiple are we really talking about? 10x? 100x? Or is it too workload-dependent to reduce to a single number?
Has someone written a good breakdown that separates true infrastructure efficiency from temporary underpricing/subsidy? The part I'm trying to understand is less ideological (local vs. cloud) and more basic economics.
I looked into the M3 Ultra 512GB Mac Studio before it was discontinued and the as best as I could determine it just wasn't worth it... yet. The GFLOPS and memory bandwidth just arne't there even though it can hold a much larger model in memory.
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.
I agree that all these datacenter companies like Coreweave are investing billions in technology that has a very fast depreciation curve and I don't know how they will sustain income. The same goes for datacenters in space, what happens when those chips are obsolete? Will they sent astronauts to replace them or will they let them burn up and send new ones into orbit every year?
I feel that the open weight models pale in comparison to the frontier models, and I believe that if the gap closes quickly, that the open weight vendors will stop releasing it for free.
Data centers in space aren’t realistic.
Higher radiation, space insulations, etc.
Underwater data centers provide a lot of the same benefits and can (much more) easily be hauled to the surface
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