I put a datacenter GPU in my gaming PC

https://blog.tymscar.com/posts/v100localllm/

by birdculture • 2 months ago

332 192 2 months ago

blog.tymscar.com

lucamark• 2 months ago

Congrats! Most people won’t want to debug drivers, kernels, ACPI, adapters, and fan headers. But for those who do, the capability-per-pound is absurd.

lelanthran6• 2 months ago

The compute is still real. The VRAM is still real. And the memory bandwidth is where it gets genuinely surprising.

Because humans write exactly like this /s

postalrat• 2 months ago

Where do you think llms learned to write that way?

alehlopeh• 2 months ago

Marketing content.

jlund-molfese• 2 months ago

You can also look at past posts by the same author (before LLM usage proliferated) if you’re curious.

The project is still very cool, but it’s a little less enjoyable to read when everything sounds the same. It would be just as annoying for people to manually write in a corporate/marketing style, because humanity is what makes the small web interesting.

https://blog.tymscar.com/posts/privategithubcicd/

lelanthran6• 2 months ago

Where do you think llms learned to write that way?

Not from individual human content, that's for sure - maybe MLM marketing copy? Sleazy 4AM ads?

I mean, every time this response comes up, I keep asking the person to point at something written prior to 2022 that gets 80%+ on the LLM detectors, and yet no one can find anything.

Maybe you, postalrat, can find something written in this style that was published prior to 2022.

tgv• 2 months ago

Because their custom training data contains an emphasis on such verbiage. It doesn't come from the God-knows-how-many TB of web content the model is pre-trained on. There, such phrasing is only a drop in the sea. But the "yes, you're right" phrases, the em dash, etc., come from the later stage, for which content is created according to some (probably overprecise) guidelines.

krackers2• 2 months ago

This particular case seems to be an LLM trying to blindly apply the saccharine therapeutic pattern "your frustration is real" to a context where it doesn't make sense. No one is debating or questioning the fact that the card has 16 gigs of vram.

The point of "X is real" in a therapeutic context is to make the person feel seen and acknowledged, that his struggles are real to him and really do weigh on his mind, even if it is technically "all in his head".

LearnYouALisp5• 1 month ago

It goes both ways, too -- people see all this on Medium, elsewhere, even in the comments on threads in YC

bossyTeacher• 2 months ago

X is Y. Z is Y. And Alpha is genuinely Beta.

Classic LLM writing style.

driverdan• 2 months ago

There's interesting stuff in this writeup but it sure seems like most of it was written by an LLM.

bitwize• 2 months ago

You know what the sad bit is? Humans do write exactly like that. That's not even particularly egregious StalkedIn marketroid speak.

matja2• 2 months ago

The AMD MI250X GPUs are also interesting - 128GB of HBM2E at 3TB/s, sometimes you see them second-hand for under $1k, the catch obviously is that it needs an OAM socket. Never seen an easy way to hook them up to a regular mainboard.

Teknomadix2• 2 months ago

These are interesting, and offer beefy through put. No point in adapting to a PCI lane thought, stuck behind the slot-bus bottleneck.

Gracana3• 2 months ago

An additional complication is that MI250Xes are two GPUs in one package, so you need to connect the first and last x16 SERDES groups to the host, otherwise you'll only see one GPU (or it won't work at all, idk).

Also, the cheap HPE pulls on eBay need some proprietary HPE magic to work, and I have yet to see anyone figure that out.

plagiarist• 2 months ago

Ahh luckily this OAM socket will prevent me from spending money.

sonzohan6• 2 months ago

This person has built a converter for the OAM socket, but it is only confirmed working with NVIDIA cards at the moment (https://www.reddit.com/r/NVIDIA_SXM2PCIE/comments/1d076cn/oa...)

It fits an MI250X, and the system sees it, but the drivers don't work. They tested an HPE MI250X. There's a rumor on the thread that there are two kinds of MI250X: Ones from HPEs and everyone else's. The HPEs require a special firmware, the normal ones do not. However, the majority of the MI250Xs on the secondhand market are HPE so caveat emptor.

knollimar2• 2 months ago

A little bit of local copium but neat read.

Isn't a rasbpi with 16gb of RAM $300 now?

matja2• 2 months ago

The latest Raspberry Pi 5 has one 32-bit channel (2x 16-bit subchannels) of LPDDR4X-4267 SDRAM giving 17.1GB/s of bandwidth, 52x less than this GPU. Never mind lacking the CUDA and Tensor cores, so the FP16 performance is 102x less (307 GFLOPS vs 31.4 TFLOPS). So for £200, there's absolutely no comparison for this specific use-case.

knollimar2• 2 months ago

Yeah thats what I'm saying. How is it so cheap????

thejj100100• 2 months ago

I don't understand what point you're trying to make here? Are you talking about the price of RAM?

jmyeet• 2 months ago

Some context:

- In 2017, the v100 was a ~$10,000 GPU. I believe there was a PCI-e version but this is probably so cheap because SXM2 is going to be harder to use;

- A 5090 has 1800GB/s of internal memory bandwidth (compared to 900GB/s in the 9 year old GPU). Of course a 5090 is substantially more expensive;

- A 5090 has ~21k CUDA cores vs ~5k;

- The current $10k NVidia GPU is the RTX 6000 Pro w/ 96GB of VRAM. It has slightly more CUDA cores but it otherwise pretty much just a 5090. This is unsurprising. NVidia uses VRAM for market segmentation.

Consider this: in 5-10 years, the trillions spent on AI data centers will likewise be sold for scrap most likely. That's how short the runway is for OpenAI and Anthropic to recover that investment.

Anyway, I'm kind of impressed the author managed to get this all to work. I don't think it even would've occurred to me that someone had made an SXM2 adapter, particularly because it's not even used anymore. Like props to whoever did that.

b112• 2 months ago

I bet 3 years, but otherwise agree.

echelon• 2 months ago

Consider this: in 5-10 years, the trillions spent on AI data centers will likewise be sold for scrap most likely. That's how short the runway is for OpenAI and Anthropic to recover that investment.

Even more interesting: it'll devalue all of SaaS and the entire US tech sector.

We might have just shot our most valuable non-AI tech products in the foot.

wholinator2• 2 months ago

How so? I understand that flooding the market with physical goods will reduce prices and thus profits. But how would that also reduce the nonphysical SAAS stuff?

mschuster914• 2 months ago

We might have just shot our most valuable non-AI tech products in the foot.

Counterpoint: the fiber buildout during the dotcom boost. That crashed the economy pretty hard when the bubble burst, but we are still benefitting from all the dark fiber that was arranged for and built out back in that era. A lot of today's ISPs were able to grab up that fiber after the bust for cents on the dollar.

Assume that OpenAI and Anthropic go bust, which at least one of them likely will, and possibly a fair few of the datacenters that are under construction will also collapse. Someone will be able to snatch these physical assets again for cents on the dollar and run open-weight models on them or train new ones.

The problem isn't (and no, this is not an AI tell, everything I write here got typed on a 2022 M2 MBA by hand) the assets, they will be put up for productive usage, just as with any other large bankruptcy or bubble in history. The problem is the "IOU" that is being passed from one hand to the next like a hot potato. Assuming a recovery of, maybe, 20% after the collapse, at 1.6 trillion dollars of assets under management by some kind of private investment/debt we're looking at about 1.3 trillion dollars in valuation that is going to be wiped out.

And given that a lot of the investment market is actually backed by pension funds... this is going to be a bloodbath. Not only will there be a lot of people laid off in addition to the layoffs we already saw "due to AI", but when the pension funds and thus their payouts collapse? We'll see retirees flooding the employment markets who just try to make a living, rendering the situation for everyone else even worse. Flipping burgers used to be a gig for students, these days students compete with people of all ages desperate to survive - and thus desperate to undercut others in wages.

Another problem will be the capacity buildout in the semiconductor industry. It's already heading toward an oligopoly after numerous boom-bust cycles: you only have two and a half GPU chip vendors (NV, AMD, Intel), two vendors of general-purpose CPU vendors (Intel and AMD - I exclude Apple because they do not sell their CPUs to any third party and ARM because 99% of non-Apple ARM chips do not go towards servers, desktops and laptops), three RAM manufacturers (Samsung, SKhynix, Micron) and two and a half physical chip manufacturers (TSMC, Samsung, Intel). When the AI bubble bursts, it will be one of a hell of an effort to prevent at least one actor from going bankrupt.

[1] https://prospect.org/2025/11/19/ai-bubble-bigger-than-you-th...

mondainx• 2 months ago

Great write-up, I've often considered these DC cards for a project and now you've convinced me to pick one up; you describe the price of the unit against what one spends on tokens and that does it for me.

tymscar18• 2 months ago

Thats why I did it. I think it’s important to put things like that into perspective

casey2• 2 months ago

Some resell group is going to have to make this easier. The shear amount of these cards otherwise heading towards the landfill is staggering. That is if Big Tech don't destroy them to prevent model weights from leaking.

eric__cartman• 2 months ago

How would destroying the GPUs prevent the model weights from leaking? By the time you get your hands on them the memory is powered off for a long enough time that a cold-boot style attack is impossible.

sethops1• 2 months ago

Would you bet your trillion dollar company on that? Or would you smash up the garbage [to you] memory chips to be sure.

Alifatisk• 2 months ago

The shear amount of these cards otherwise heading towards the landfill is staggering.

The thought of throwing away working cards sounds so bizarre to me. I can't believe companies would dispose them into the landfill like that, it is at least worth giving away for refuse.

wookmaster• 2 months ago

There’s a long history of corporations doing evil things to ensure their business model succeeds

Gracana3• 2 months ago

Things like this have started to show up on eBay: https://www.ebay.com/itm/198383386991

  2X NVIDIA Tesla V100 32GB NVLink Water Cooled X99 E5-2686v4 AI Workstation PC

  Item                              Quantity
  Intel Xeon E5-2686 v4 CPU           1
  2U CPU Cooler                       1
  Jingyue X99 Motherboard             1
  DDR3 Memory                         32GB
  SSD                                 480GB
  AMD Radeon R5 240 4K Display Card   1
  NVIDIA Tesla V100 32GB SXM2 GPU     2
  NVLink SXM2 Dual-GPU Baseboard      1
  Corsair Water Cooling System        2
  850W Bronze Power Supply            1
  Dual-GPU 300G NVLink SXM2 Baseboard 1
  8654 Data Cable                     2
  8654 to PCIe Adapter Card           1

segmondy3• 2 months ago

terrible deal

xioxox• 2 months ago

Isn't this the same thing with 32 GB already on a PCIe socket?

https://www.ebay.com/itm/166850431555

segmondy3• 2 months ago

kinda, they put that on a PCIe socket, but it's passive. Meaning no fan. If you try inference on that it overheats in 1 minute unless you have it inside a server case.

iugtmkbdfil8347• 2 months ago

I genuinely hope that is the case. The market is absolutely bananas now. I actually now own devices that went up in 'value' since purchase. This is not normal ( and a little scary ). This, on the other hand, is an invitation to properly recycle otherwise unwanted hardware.

Teknomadix2• 2 months ago

Tesla V100 SXM2 16GB is NOT DGX class as the author writes. It's HGX class. The V100 comes in two classes, SXM2 and SXM4, the latter coming with a Max of 80gb on board memory. Typically these are installed 8×A100 80GB SXM4 on an HGX riser, and what that gives you is NVSwitch fabric and 640GB of pooled HBM2e (on package stacked memory /w ~2 TB/s of memory bandwidth). 2u standard rack footprint too.

legitronics• 2 months ago

I have no idea what you are trying to say.

V100 came as sxm2 and sxm3. And it was 16 and 32gb.

HGX is DGX with extra toppings.

_zoltan_2• 2 months ago

No. DGX is when it's Nvidia's design, HGX when it's a 3rd party design.

_zoltan_2• 2 months ago

What on earth are you talking about? Your comment makes no sense.

The V100 and A100 are different generations altogether.

The V100 does not have 2TB/s.

omarqureshi3• 2 months ago

Could probably avoid the crazy fan with a waterblock - I've seen a whole kit, v100 + PCIE adapter + block for £235. Yes, you'll have to pay for pump, radiators and radiator fans, but that should really quieten it down

pogue2• 2 months ago

Someone's already made such a kit as you describe to fit in a consumer PC case and work properly?

omarqureshi3• 2 months ago

https://www.ebay.co.uk/itm/406939340557?var=677143153030&mke...

omarqureshi3• 2 months ago

32 GB kit as well

https://www.ebay.co.uk/itm/406939344915?_trkparms=amclksrc%3...

recursivegirth• 2 months ago

The compute is still real. The VRAM is still real. And the memory bandwidth is where it gets genuinely surprising.

Had to stop there. Annoying. I can't stand AI use for writing. It makes any otherwise great article feel so disingenuous.

m0rde2• 2 months ago

What a difficult world you must live in these days

peddling-brink• 2 months ago

While I don’t disagree with their sentiment, I’m far more annoyed with it than the AI writing.

fouc3• 2 months ago

That line was the exact moment I also realized the post was AI written. I kept reading though, but I am left constantly guessing at which key details might be pure hallucinations.

SubiculumCode• 2 months ago

Honestly, the default styles are pretty bad. I use Claude in my scientific writing in a very specific way. 1. I write a paragraph. 2. I put Claude into concise style mode. I then ask Claude to revise for clarity.

I can write competently, but it's natural direction is towards emotional rhythmic flow that can convey emotion/passion...but which for scientific writing, can get in the way of clear clean communication. So, I write what I mean,and Claude straightens it out...and these days (i.e. not last year), it doesn't lose my meaning that often. And since I wrote it first, these AI-isms appear less frequently, and if they do, I revise them away.

tymscar18• 2 months ago

FYI, not a single line was AI written. If there is a hallucination, it’s fully mushy brain sourced.

tymscar18• 2 months ago

Agree. But I have not used AI in the slightest.

Some of us just write that. AIs had to learn it from somewhere.

mickeyp• 2 months ago

Impressive work. But the problem is not the 30 tok/s which is fine for agentic coding and chat.

It's prefill; slow prefill kills agentic workloads dead.

If you have 100,000 tokens at ~150tok/s per the OP, you're looking at:

    You have: 100000 / (150/s)

    You want: hms

     11 min + 6.6666667 sec

Which is quite a wait indeed.

Aurornis• 2 months ago

Most people won’t be dumping 100K tokens into it at once, but I agree that all of the prefill time that adds up during a session becomes a lot to account for.

This is also a problem for all of the Mac local LLMs. Macs are a great way to get a lot of high bandwidth memory, but their compute is very far behind current gen dedicated GPUs. Some of the expensive Mac Studio setups allow you to run very large models with usable tokens/s, but you can be waiting a long time for it to get to the point of generating those tokens.

Tepix• 2 months ago

When you're using OpenCode it's easy to reach 100,000 tokens after a while.

pyrolistical• 2 months ago

The prefix cache is working properly 100k doesn’t prefill more than once

HarHarVeryFunny• 2 months ago

I wonder if this could be usefully mitigated with a combination of prompt (prefix) caching and an agent that let you control what the prompt prefix consisted of. The goal would be to incur that slow prefill once to build the prompt cache, then have subsequent prompts consist of mostly this fixed prefix plus specific instructions.

For a language like C++ where modules are split into definition (.h) and implementation (.cpp) parts, one choice of prefix would be all the header files for the project (which aren't likely to change much).

More generally the idea would be to have an agent that had cached-prefix reuse as it's primary context management goal.

Another possibility, to support caching of files that have since changed, would be for the agent to build the context as a fixed prefix reflecting some or all of the codebase in its start-of-session state, then append any changes to that, with appropriate prompting to only use the latest definition of a function.

e.g.

Say file A initially contains functions X, Y and Z, then the prompt prefix is built to include X Y Z. If the user then modifies Y -> Y', then just add that to the context, so that the cached prefix is unchanged, giving X Y Z Y'.

pastage• 2 months ago

A quick search say that this is a standard feature you cache the prefill and load it at PCIe bandwidth so it should be about 0.2s

anigbrowl2• 2 months ago

Can't you structure things like loading a codebase or priming with reference material to happen overnight or during meal breaks etc? I guess it's frustrating if you want to switch to a project and have the LLM begin co-working with immediately, but even the best human collaborator would require a long period to get up to speed before being able to make meaningful contributions.

whoamii• 2 months ago

The real question: did your local LLM write this post?

20wenty• 2 months ago

There are many tells aren't there? There was clearly hard human work and experimentation here, but it's a shame the OP let AI do chunks of the writing. Once you see it, it's much harder to take the post seriously.

iugtmkbdfil8347• 2 months ago

I disagree. Not everyone has a good writing style. In those instances I think it is fair to default to llm recommendation. We may be allergic to it, but we saw one formulaic response too many ( though admittedly it does raise a question of whether HN was the intended audience for it ).

In any event, not all of us have a unique writing style worth preserving just like not all of us can write clear and clean code. Just saying.

xp84• 2 months ago

(TL;DR Can we just judge written works by their actual content?)

I’m really in the “who gives a shit” camp on something like this. A lot of people probably have an LLM punch up a blog post. It is good at turning bullet points and notes into prose, fixing run-ons, etc. Maybe I’m naive but I trust that the kind of person who posts a clearly noncommercial post like this on HN gives a crap enough that they read the final draft and confirmed it isn’t inaccurate.

This pearl-clutching about the mere use of AI regardless of how responsible or appropriate the use is, seems like a professor in 1985 throwing an essay back in a student’s face as “this was obviously printed from a computer and not typewritten like a PROPER essay! I can tell just by looking at it!”

tymscar18• 2 months ago

Not at all, no. I had this chat before about how I am one of those unlucky few that loved the way LLMs write nowadays since the mid-2000s.

Slowly but surely, I had to remove my beloved lists, emojis (though LLMs do less of that now, maybe I can incorporate them back), and emdashes.

lowbloodsugar• 2 months ago

Oh then please stop reading. There are many of us who are really good at solving complex problems and also really bad at communicating them. Your attitude is just the latest bastion of bigotry. So do feel free to self-select out of useful knowledge and experience.

Gormo• 2 months ago

There are many tells aren't there?

There are no usable tells that apply generally in the first place. Pretty much all of the hyped-up memes in circulation about how to detect LLM output are highly unreliable.

LLMs write the way they do because they are trained on common patterns of human writing. All of the tropes people point to in LLM output are there precisely because they've already been in widespread use for some time.

bob1029• 2 months ago

And yes, if you want the absolute best, Opus 4.8 exists. It also costs more per 20 minutes of heavy use than I paid for this entire GPU and adapter setup combined. But the gap is shockingly small.

I don't think this is a fair characterization of the situation. I use frontier models via API pre-paid tokens every single day, and I can barely rack up $100 per month. The fact that we figured out how to burn double this in 20 minutes is impressive, but I don't think it reflects the reality that many are experiencing right now. There are some exceptionally gluttonous approaches to harnessing LLMs that I think are serving as convenient straw men in these discussions.

Paying for the API will almost always be more economical than self-hosting equivalent infrastructure. I am not against self-hosting, but the article suggests a primarily economic motivation for this effort. If you are consuming fewer than 10^9 tokens per month, I really don't think it's worth your time to try and compete with the hyperscalars. Most of the money is to be found in the integration of this technology with existing businesses.

vidarh2• 2 months ago

I use hosted providers myself, but I can churn through $100 worth of tokens in half a day even with cheap models like Deepseek easily. If someone's use is as light as yours, then sure - grab a subscription and you'll save far more. For higher use it will come down to how cheap your electricity is whether it is worth offloading at least some of it (for me it's not, FWIW)

iJohnDoe• 2 months ago

Could you share a bit about what you’re working on or what type of projects require that much usage? Is it hobby, production, revenue generating?

solenoid0937• 2 months ago

Same, very surprised when people on HN are shocked by high token burn - it's really not hard if you've figured out how to use LLMs!

oceanplexian• 2 months ago

Claude is something like $35 per million tokens. If I was using API pricing I could trivially spend $100 in a single hour long coding session, with /fast turned on in about 10 minutes. Not sure how you guys are using it.

foolfoolz• 2 months ago

coding is the easy part of using claude

MattRix2• 2 months ago

Opus is normally $5 per mtok, no idea why anyone would use /fast if they were at all concerned about price. ($5 is still pricy though tbh)

KronisLV2• 2 months ago

I use frontier models via API pre-paid tokens every single day, and I can barely rack up $100 per month.

According to ccusage (https://github.com/ryoppippi/ccusage) if I didn’t have the 100 USD Max subscription, I’d have to pay Anthropic around 4173 USD for the month of May.

  Input     │ Output     │ Cache Create │ Cache Read    │ Total Tokens  │ Cost (USD)
  1,948,016 │ 19,435,081 │ 103,626,350  │ 6,244,194,278 │ 6,369,203,725 │ $4173.09

Edit: pulled the latest numbers, not using Fast mode at all, but still Opus for most tasks.

Nothing too egregious with my usage patterns, typically Claude Code just churning tasks in 1-2 projects at a time, sometimes while I’m asleep - and I hit around 60-80% of the weekly caps most of the time.

bloudermilk• 2 months ago

How do you orchestrate this? I’m on max and would love to be hitting my caps when I’m not actively working a project

ewy1• 2 months ago

despite gaming being used in the title, it is not mentioned in the article, but i'm curious how this performs.

i've ran some multi vendor frankenstein setups before and sometimes it even works, so i'm curious to hear your experience with it.

pogue2• 2 months ago

But could you game with the GPU? Or is that purely a drivers issue?

mcraiha• 2 months ago

I assume you can game with it if you use modded drivers. At least with CMP 50HX that is possible https://videocardz.com/newz/nvidia-cmp-50hx-turned-into-gami...

abejfehr• 2 months ago

Based on the title I was really hoping to see how this was used for gaming, but they just ran an LLM on it

axpy9062• 2 months ago

Same. With no new NVIDIA gaming GPUs this year, seems like an interesting problem to solve.

mschuster914• 2 months ago

I don't think that is even possible, every piece of silicon on that chip that is required to do gaming is ripped out in favor of more compute cores.

darkwater• 2 months ago

They said in the beginning that it doesn't even have a video out, so you cannot do gaming.

yjftsjthsd-h2• 2 months ago

I thought you could run games by rendering on one GPU and outputting on another? Usually comes up with dual iGPU/dGPU setups, but could work here

toast0• 2 months ago

I've seen things where you have multiple video cards and can use one gpu to render to a framebuffer which is transferred to the other video card to output. I'm sure it adds latency, and it's probably unsupported... But no output doesn't mean can't do gaming... It just means gaming will be iffy.

There's some virtualized desktop server stuff too. Run a bunch of desktop sessions on a beefy computer and send a video stream to desktop players. With the right codec settings, the latency is probably ok for many games.

hakfoo2• 2 months ago

I'm actually surprised there hasn't been a dedicated effort to support display offload to, say, the CPU's iGPU.

I'm sure manufacturers would love saving a dollar per card, and OEMs would appreciate eliminating the support calls from "I just bought a new $2500 gaming PC and no video" because they plugged the monitor into the iGPU instead of dGPU.

lightedman• 2 months ago

Never a problem. RemoteFX does (did) everything you'd want. Make your OS, log in remotely through an accelerated client. The real problem is Microsoft did something around Windows Server 2008 R2 that killed performance (literally halved it) for RemoteFX. You're only now reobtaining the virtualized video performance we used to have back in 2008.

wg0• 2 months ago

Wait a few years, everyone will be able to put one at half the price.

KnuthIsGod• 2 months ago

AI written posts will kill HN.

tymscar18• 2 months ago

AI didnt edit a single word of this post.

axpy9062• 2 months ago

Wow. V100. That brings back memories. Way to go.

gtirloni• 2 months ago

The compute is still real. The VRAM is still real. And the memory bandwidth is where it gets genuinely surprising.

sigh

krackers2• 2 months ago

Usually that cloying pattern is reserved for "emotional" contexts to validate the user ("your struggles are real [despite others thinking it's in your head]").

Here it doesn't even make sense, of course the VRAM is real. Is it going to tell me that my keyboard is real next?

I wonder if this was generated with the local model, this seems to be a case where it memorized the style but not the meaning and intent.

viseyth• 2 months ago

Volta (and Pascal, which I'm using) should still be supported with driver 580 as long as you don't use the open modules, and you can use up to cuda 12.9 and cudnn 9.10.2. No need to limit yourself to an old kernel.

markus92• 2 months ago

It is. We still run quite a few of them in prod and with 580 drivers they run just fine. Very useful GPUs still.

segmondy3• 2 months ago

The most interesting and perhaps useful for most would be how they control the fan. If you are thinking of doing this, you really want to get those fans under control, they are loud. For anyone thinking of these, v100s idle super high! 25-35watt with nothing loaded and easily 50w when a model is loaded.

00dazzle• 2 months ago

That's the same price per VRAM GB as an arc pro B70

tymscar18• 2 months ago

But with miles better support, thats why I went this route. Cuda is hard to beat

jonhohle• 2 months ago

I was just looking into this and was worried about the fan setup. Interesting that he was able to solve it with good results.

In case anyone is interested, I’m using PCIE passthrough on a FreeBSD host to a Linux guest with an older Pascal card. It’s worked great and I’ve been thinking about putting a nicer card in there. The SXM route seems great, but I’ve been burned (almost literally because of the heat) by DC components before.

sonzohan6• 2 months ago

I also recently decided to buy a datacenter GPU and slap it into a system. Some notes from my experience that the author doesn't mention in their article:

Decommissioned NVIDIA V100s and AMD MI50s are fairly cheap, $200 for 16gb and $400-500 for 32gb, for local experimentation. They are also very old. There's an enthusiast community keeping these two cards alive and working with current platforms and models.

Nitpick, but the V100 doesn't support bfloat16. The performance hit is not a big deal if you're fiddling with local models, but the card is on it's way out in terms of hardware features.

The MI50 does support bf16, but not the current edition of AMD ROCm. Vulkan support is good and the MI50 works with most major platforms (llama.cpp, vllm, etc.), but it's not without some pain points like manual recompilation. Fortunately the open source community has already paid most of your way.

The cooling requirements for these cards cannot be understated. A consumer grade GPU may throttle if in a small case without additional fans, but if given the same treatment a datacenter GPU will overheat itself idling. You will need to buy, at least, a bunch of decent 120mm fans to prevent this or invest in some water cooling.

I ultimately went with an AMD MI100 32GB ($950). I'm an AMD fan, current ROCm editions support it, and it was low-fuss to get things working. I'm debating getting a second so I can try out bigger models like qwen3-coder-next.

Silagi• 2 months ago

Did you consider the R9700 or B70 when you went for the MI100? If so, what made you choose the MI100?

I've been playing with picking up a card in this class but haven't been able to justify it when running the Qwen3.6 MOE model on a 6800xt is tolerable for the type of projects I've been willing to point local AI at.

sonzohan6• 2 months ago

I looked at those, the Arc 1100, the w6800, MI50, MI60, v100, v620, and basically anything with 32gb of RAM:

1. I wanted an AMD card.

2. I have an RTX 3090 that's been fun to play with, but I want to get back to using it for gaming.

3. I was looking for between 30-60 tokens/second in terms of performance on the beefier models I want to run. Looking at stock Qwen3 32B the benchmarks reported about 41 tokens/second for MI100. w6800 was 18, MI50 & MI60 could do 60s but had a lot of compromises/special things to achieve that.

4. I used FitMyLLM for some spec-based comparisons (https://www.fitmyllm.com/). The MI100 is roughly double the performance on Qwen 3.5 35B A3B Q5_K_M to the R9700 (462 token/s prefill vs 239 tokens/s, 217 tokens/s vs 118 token/s for inference)

5. I was willing to throw up to $1k at a GPU; I really wanted to throw closer to $650.

To be honest, if money was no objection I would've sprung for a MI210. I also considered the MI250 as they showed up for $1250-1400 with a whopping 128GB, but the PCIE converters for that form factor don't have working AMD drivers yet.

doubled112• 2 months ago

if given the same treatment a datacenter GPU will overheat itself idling

I have a friend who has learned this through several server grade cards over the years.

Yes your Intel 10G NIC was cheap. No you cannot just stick it in your desktop. It is expecting server level airflow, probably with a cold intake side.

He printed a fan mount, slapped it on, and they’ve been happy together since.

cthalupa3• 2 months ago

I've had multiple X520-DAs in desktop cases for years without doing anything special for cooling. Hell, one of them was in a fully watercooled system that had very little in-case airflow.

gerdesj• 2 months ago

Show your friend QSFP and QSFP+ ...

kethinov2• 2 months ago

qwen3-coder-next runs fine on my consumer grade nvidia 4070. Performance is not spectacular, but it's only a little bit slower than a properly-fit model.

sonzohan6• 2 months ago

What are your settings and tokens/second? Even with 2 GPUs (MI100, RX 6600 XT 8GB) and 32GB of RAM it was running at a snails pace for me.

I didn't try a sched_spread with a 3090 and the MI100 which would provide 56GB ram

overfeed• 2 months ago

You will need to buy, at least, a bunch of decent 120mm fans to prevent this or invest in some water cooling

There's a cottage industry of 3D-printed fan-shrouds for data center GPUs - 120mm are often the sweet spot for quietness and practicality. The shoud smugly fits the GPUs intake, so it gets all the airflow from the attached fan(s), whose speed curves can be attached to GPU temperature.

sonzohan6• 2 months ago

Shoutout to the makers and hackers supplying those!

mettamage• 2 months ago

The way it works is that a vision encoder (similar to what ChatGPT and Claude use) takes image pixels and translates them into the LLM’s token embedding space. The model does not “see” the image the way a human does. Instead, the vision encoder compresses the image into a sequence of vectors that live in the same mathematical space as text tokens. The LLM then processes those vectors as if they were just another sequence of tokens.

Could you also do this for music and specifically sound synthesis? It would be awesome to vibe synthesize sounds and then see the VSTi parameters surrounding it.

Ey7NFZ3P0nzAe3• 2 months ago

I don't think so. Cramming new senses into the latent space of the model is one thing, but having a model output tokens that can be detokenized into sound is completely different and requires a very different type of data.

LearnYouALisp5• 1 month ago

What do you mean? Why not? We can already FFT sound into "words", so why not have some kind of dictionary to an arbitrary level of precision/fidelity?

jeffrallen• 2 months ago

Super interesting. I use data center GPUs at work, but I didn't know anything about this stuff.

I also use Qwen 3.7 27b at work and I agree with the author: it is perfectly capable of the jobs I give it.

peibye• 2 months ago

All that work just to write an ai blog post. This is a cool topic but I just can’t deal with the aiisms.

melonpan7• 2 months ago

Great value for money if you have the time for tinkering and getting the compatibility to work.

DANmode• 2 months ago

and it only gets easier and more defined from here.

tymscar18• 2 months ago

If you use NixOS, you don’t need to do any of that anymore. A single command in the CLI, and you have everything I do.

drumhead• 2 months ago

The first dual core processor I built a machine with was an Opteron. It was a nice piece of hardware.

LearnYouALisp5• 1 month ago

xw9300 fan

suralind• 2 months ago

This is great! I've been trying to get into local models for a while as I share the sentiment that local models will eventually be so good that there won't be a need to use frontier models for most coding tasks (perhaps that's already true today?).

I have zero experience building computers - where would I even start? I mean, aside from the things already well documented and mentioned in the blog post.

tymscar18• 2 months ago

Building computers is very easy. I would suggest watching a YouTube video to get the general gist, and then once you buy the parts, just Google for whatever doesn’t go well.

I built my first Pentium 4 one when I was like six, so I’m sure someone much older that’s into tech can do it without an issue.

There are also tons of Discord communities that are willing to help you live if you encounter any issues.

binyu5• 2 months ago

The V100 and the 4090 are based on vastly different architectures, the former uses the older Volta while the latter uses Ada. Last I checked you cannot meaningfully combine them. The 3090 is better than the V100, just get two 3090 and a NVLink.

tymscar18• 2 months ago

Well I did in fact meaningfully combined them without an issue, that was the whole point of the blogpost.

binyu5• 2 months ago

Yes but it creates a bottleneck that negates the benefit of using multiple cards that way. Look into it. Cheers

cthalupa3• 2 months ago

You can split tensors across an AMD GPU and Nvidia GPU - different architectures are not an issue. People run LLMs across some pretty crazy setups.

binyu5• 2 months ago

It depends but you cannot directly mix for example Ampere with Ada coz the lack of support for native FP8 in Ampere.

QuantumNoodle• 2 months ago

This is very cool. Can anyone point me to any resources for setups that can ron decent inferencing at home?

mg794613• 2 months ago

Very interesting, but slight note on the fact that writer sort of forgets to calculate in the price of the 4080 it works together with.

He spend 200 to upgrade his existing setup, great nonetheless. But not "32gb gram for only 200 bucks"

tymscar18• 2 months ago

I did mention that in my case I had the 4080, but you do not need the 4080 whatsoever. You can run the same model with less context on a single £200 V100 or with the same amount of context on two of these.

rbanffy2• 2 months ago

This lack of driver support for hardware that’s still functional and available in reasonable quantities is annoying.

I was very close to buying a retired POWER7+ server with an ungodly amount of memory, but decided being unable to run a modern Linux kernel would be more work than I wanted to have. Modern kernels need POWER8 and above.

rbanffy2• 2 months ago

This lack of driver support for hardware that’s still functional and available in reasonable quantities is annoying.

OTOH, if these chips were fully supported, they wouldn’t hit the second hand market at the prices they do.

neutrinobro• 2 months ago

Very nice to see this older hardware getting repurposed. I have been running 2x Tesla V100s in a dual-core supermicro X10DRU-i server. With qwen3.6-27B-mtp I get about 35-40tok/s for inference for moderate context sizes (<128k), and have run long running agent tasks on it which consume 100s of millions of tokens (>$100s if I had to pay claude API costs). However, the main purpose that I have to for these cards is for scientific compute, the FP64 performance (7+ TFLOPS!) is fantastic given their age, and not something you can get on even the latest consumer grade cards since Nvidia nerfed their performance after Kepler. The server lives in the basement though...it is freaking loud!

Ey7NFZ3P0nzAe3• 2 months ago

While we're at it, has anyone any tips on turning a consumer RTX 3090 into a blower type format for putting inside a server rack? Any kind of workaround like this.

j4k0bfr• 2 months ago

Thank you for writing this! And sharing the NixOS config. I've been looking for an excuse to get into Nix for ages, but this might finally do it.

P.S. Man the AI writing complaints on this thread are quite upsetting. OP is handling it a lot better than I would, lol.

AI2070• 2 months ago

now design a shroud and blower

I put a datacenter GPU in my gaming PC

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