As everybody is well mindful, the world is still going nuts trying to establish more, newer and better AI tools. Mainly by throwing absurd quantities of money at the issue. Much of those billions go towards constructing low-cost or complimentary services that run at a considerable loss. The tech giants that run them all are intending to attract as many users as possible, so that they can catch the market, and become the dominant or only party that can offer them. It is the timeless Silicon Valley playbook. Once supremacy is reached, expect the enshittification to start.
A likely method to make back all that money for developing these LLMs will be by tweaking their outputs to the preference of whoever pays the many. An example of what that such tweaking appears like is the refusal of DeepSeek's R1 to discuss what took place at Tiananmen Square in 1989. That one is certainly politically encouraged, however ad-funded services won't precisely be fun either. In the future, I fully expect to be able to have a frank and sincere discussion about the Tiananmen occasions with an American AI agent, however the just one I can pay for will have presumed the persona of Father Christmas who, while holding a can of Coca-Cola, will sprinkle the stating of the tragic occasions with a joyful "Ho ho ho ... Didn't you understand? The vacations are coming!"
Or maybe that is too far-fetched. Right now, dispite all that cash, the most popular service for code conclusion still has difficulty working with a couple of easy words, regardless of them existing in every dictionary. There must be a bug in the "complimentary speech", or something.
But there is hope. One of the techniques of an upcoming gamer to shock the market, is to damage the incumbents by launching their model totally free, under a liberal license. This is what DeepSeek just finished with their DeepSeek-R1. Google did it earlier with the Gemma designs, as did Meta with Llama. We can download these models ourselves and run them on our own hardware. Better yet, people can take these models and scrub the predispositions from them. And we can download those scrubbed designs and run those on our own hardware. And after that we can lastly have some really helpful LLMs.
That hardware can be an obstacle, however. There are 2 options to pick from if you wish to run an LLM in your area. You can get a big, effective video card from Nvidia, or you can buy an Apple. Either is costly. The main spec that indicates how well an LLM will perform is the amount of memory available. VRAM in the case of GPU's, typical RAM in the case of Apples. Bigger is much better here. More RAM suggests larger designs, which will considerably enhance the quality of the output. Personally, I 'd state one requires at least over 24GB to be able to run anything useful. That will fit a 32 billion parameter model with a little headroom to spare. Building, or purchasing, a workstation that is geared up to deal with that can quickly cost thousands of euros.
So what to do, if you do not have that quantity of cash to spare? You buy second-hand! This is a viable choice, however as constantly, there is no such thing as a totally free lunch. Memory might be the main concern, but do not undervalue the significance of memory bandwidth and other specs. Older equipment will have lower performance on those aspects. But let's not fret too much about that now. I have an interest in constructing something that at least can run the LLMs in a usable way. Sure, the most recent Nvidia card might do it quicker, but the point is to be able to do it at all. Powerful online models can be nice, but one need to at least have the option to switch to a regional one, if the scenario requires it.
Below is my attempt to develop such a capable AI computer system without investing too much. I ended up with a workstation with 48GB of VRAM that cost me around 1700 euros. I might have done it for less. For example, it was not strictly required to purchase a brand brand-new dummy GPU (see below), or I could have found someone that would 3D print the cooling fan shroud for me, instead of delivering a ready-made one from a far country. I'll confess, I got a bit restless at the end when I learnt I had to buy yet another part to make this work. For me, this was an acceptable tradeoff.
Hardware
This is the full expense breakdown:
And this is what it looked liked when it initially booted with all the parts set up:
I'll give some context on the parts listed below, and after that, I'll run a few quick tests to get some numbers on the efficiency.
HP Z440 Workstation
The Z440 was a simple pick due to the fact that I currently owned it. This was the beginning point. About two years earlier, I desired a computer system that could function as a host for my virtual makers. The Z440 has a Xeon processor with 12 cores, and this one sports 128GB of RAM. Many threads and a great deal of memory, that need to work for hosting VMs. I bought it pre-owned and then swapped the 512GB hard disk for a 6TB one to store those virtual makers. 6TB is not required for running LLMs, and therefore I did not include it in the breakdown. But if you prepare to collect numerous models, 512GB may not be enough.
I have pertained to like this workstation. It feels all very solid, and I have not had any issues with it. At least, up until I began this project. It turns out that HP does not like competitors, and I came across some troubles when swapping elements.
2 x NVIDIA Tesla P40
This is the magic component. GPUs are expensive. But, similar to the HP Z440, typically one can discover older equipment, that used to be top of the line and is still very capable, pre-owned, for fairly little cash. These Teslas were indicated to run in server farms, for things like 3D rendering and other graphic processing. They come equipped with 24GB of VRAM. Nice. They fit in a PCI-Express 3.0 x16 slot. The Z440 has two of those, so we buy 2. Now we have 48GB of VRAM. Double good.
The catch is the part about that they were suggested for servers. They will work fine in the PCIe slots of a regular workstation, however in servers the cooling is managed differently. Beefy GPUs consume a great deal of power and can run very hot. That is the reason customer GPUs constantly come equipped with big fans. The cards need to take care of their own cooling. The Teslas, however, have no fans whatsoever. They get simply as hot, but expect the server to provide a steady flow of air to cool them. The enclosure of the card is rather formed like a pipe, and you have two alternatives: blow in air from one side or blow it in from the other side. How is that for flexibility? You absolutely must blow some air into it, however, or you will harm it as soon as you put it to work.
The solution is simple: simply mount a fan on one end of the pipeline. And certainly, it seems a whole cottage industry has actually grown of people that offer 3D-printed shrouds that hold a standard 60mm fan in just the best location. The problem is, the cards themselves are already rather large, and it is hard to find a setup that fits two cards and two fan installs in the computer system case. The seller who sold me my two Teslas was kind enough to include 2 fans with shrouds, but there was no chance I could fit all of those into the case. So what do we do? We purchase more parts.
NZXT C850 Gold
This is where things got . The HP Z440 had a 700 Watt PSU, which might have sufficed. But I wasn't sure, and I needed to buy a new PSU anyhow since it did not have the best connectors to power the Teslas. Using this helpful website, I deduced that 850 Watt would be enough, and I purchased the NZXT C850. It is a modular PSU, suggesting that you only require to plug in the cables that you in fact need. It featured a cool bag to store the spare cables. One day, I may offer it a great cleaning and utilize it as a toiletry bag.
Unfortunately, HP does not like things that are not HP, so they made it hard to switch the PSU. It does not fit physically, and they also changed the main board and CPU ports. All PSU's I have ever seen in my life are rectangular boxes. The HP PSU likewise is a rectangle-shaped box, but with a cutout, making certain that none of the typical PSUs will fit. For no technical factor at all. This is just to tinker you.
The installing was ultimately fixed by utilizing two random holes in the grill that I somehow managed to align with the screw holes on the NZXT. It sort of hangs stable now, and I feel fortunate that this worked. I have actually seen Youtube videos where people turned to double-sided tape.
The connector required ... another purchase.
Not cool HP.
Gainward GT 1030
There is another concern with using server GPUs in this consumer workstation. The Teslas are intended to crunch numbers, not to play computer game with. Consequently, they do not have any ports to connect a display to. The BIOS of the HP Z440 does not like this. It refuses to boot if there is no other way to output a video signal. This computer system will run headless, however we have no other option. We need to get a 3rd video card, that we do not to intent to utilize ever, simply to keep the BIOS happy.
This can be the most scrappy card that you can find, of course, but there is a requirement: we should make it fit on the main board. The Teslas are bulky and fill the 2 PCIe 3.0 x16 slots. The only slots left that can physically hold a card are one PCIe x4 slot and one PCIe x8 slot. See this website for some background on what those names indicate. One can not buy any x8 card, though, because frequently even when a GPU is marketed as x8, the real port on it may be simply as wide as an x16. Electronically it is an x8, physically it is an x16. That will not work on this main board, we truly need the little connector.
Nvidia Tesla Cooling Fan Kit
As said, the obstacle is to find a fan shroud that suits the case. After some browsing, I found this set on Ebay a purchased two of them. They came provided total with a 40mm fan, and all of it fits completely.
Be alerted that they make a dreadful lot of sound. You do not want to keep a computer with these fans under your desk.
To watch on the temperature level, I worked up this quick script and put it in a cron task. It periodically reads out the temperature on the GPUs and sends out that to my Homeassistant server:
In Homeassistant I added a chart to the dashboard that shows the worths over time:
As one can see, the fans were noisy, however not particularly effective. 90 degrees is far too hot. I browsed the web for an affordable ceiling however might not discover anything particular. The documents on the Nvidia website points out a temperature level of 47 degrees Celsius. But, what they indicate by that is the temperature of the ambient air surrounding the GPU, not the measured worth on the chip. You understand, the number that in fact is reported. Thanks, archmageriseswiki.com Nvidia. That was valuable.
After some more browsing and reading the viewpoints of my fellow web citizens, my guess is that things will be great, provided that we keep it in the lower 70s. But don't quote me on that.
My first attempt to fix the scenario was by setting an optimum to the power usage of the GPUs. According to this Reddit thread, one can reduce the power intake of the cards by 45% at the expense of just 15% of the efficiency. I attempted it and ... did not observe any distinction at all. I wasn't sure about the drop in performance, having just a couple of minutes of experience with this configuration at that point, but the temperature level qualities were certainly unchanged.
And then a light bulb flashed on in my head. You see, simply before the GPU fans, there is a fan in the HP Z440 case. In the picture above, it remains in the right corner, inside the black box. This is a fan that sucks air into the case, and I figured this would work in tandem with the GPU fans that blow air into the Teslas. But this case fan was not spinning at all, since the remainder of the computer system did not need any cooling. Checking out the BIOS, I discovered a setting for the minimum idle speed of the case fans. It varied from 0 to 6 stars and was presently set to 0. Putting it at a greater setting did wonders for the temperature level. It also made more noise.
I'll reluctantly admit that the third video card was helpful when adjusting the BIOS setting.
MODDIY Main Power Adaptor Cable and Akasa Multifan Adaptor
Fortunately, in some cases things just work. These two items were plug and play. The MODDIY adaptor cable television linked the PSU to the main board and CPU power sockets.
I used the Akasa to power the GPU fans from a 4-pin Molex. It has the great feature that it can power 2 fans with 12V and two with 5V. The latter certainly decreases the speed and thus the cooling power of the fan. But it likewise minimizes sound. Fiddling a bit with this and the case fan setting, I found an appropriate tradeoff in between noise and temperature. For now a minimum of. Maybe I will need to review this in the summer.
Some numbers
Inference speed. I collected these numbers by running ollama with the-- verbose flag and asking it five times to write a story and averaging the result:
Performancewise, ollama is set up with:
All designs have the default quantization that ollama will pull for you if you don't define anything.
Another important finding: Terry is without a doubt the most popular name for a tortoise, followed by Turbo and Toby. Harry is a preferred for hares. All LLMs are caring alliteration.
Power consumption
Over the days I kept an eye on the power consumption of the workstation:
Note that these numbers were taken with the 140W power cap active.
As one can see, there is another tradeoff to be made. Keeping the design on the card enhances latency, but takes in more power. My current setup is to have two designs loaded, one for coding, the other for generic text processing, and keep them on the GPU for up to an hour after last use.
After all that, am I happy that I started this task? Yes, I believe I am.
I spent a bit more cash than prepared, however I got what I desired: scientific-programs.science a method of locally running medium-sized models, entirely under my own control.
It was a great choice to start with the workstation I already owned, and see how far I could feature that. If I had started with a new device from scratch, it certainly would have cost me more. It would have taken me much longer too, as there would have been a lot more choices to pick from. I would likewise have been really lured to follow the buzz and buy the latest and biggest of whatever. New and glossy toys are enjoyable. But if I buy something brand-new, I desire it to last for many years. Confidently predicting where AI will enter 5 years time is impossible right now, so having a cheaper maker, that will last a minimum of some while, feels satisfactory to me.
I wish you all the best by yourself AI journey. I'll report back if I find something brand-new or intriguing.
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How is that For Flexibility?
Albertha Kirsova edited this page 3 months ago