As everyone is well mindful, the world is still going nuts trying to establish more, more recent and much better AI tools. Mainly by tossing absurd amounts of cash at the problem. A number of those billions go towards developing inexpensive or free services that run at a considerable loss. The tech giants that run them all are wishing to bring in as many users as possible, so that they can capture the marketplace, and become the dominant or only celebration that can provide them. It is the timeless Silicon Valley playbook. Once dominance is reached, expect the enshittification to begin.

A likely way to make back all that money for establishing these LLMs will be by tweaking their outputs to the preference of whoever pays the a lot of. An example of what that such tweaking appears like is the rejection of DeepSeek's R1 to discuss what occurred at Tiananmen Square in 1989. That one is certainly politically encouraged, however ad-funded services won't precisely be enjoyable either. In the future, I completely expect to be able to have a frank and sincere discussion about the Tiananmen events with an American AI representative, but the just one I can afford will have assumed the personality of Father Christmas who, while holding a can of Coca-Cola, will sprinkle the recounting of the terrible occasions with a joyful "Ho ho ho ... Didn't you understand? The vacations are coming!"

Or perhaps that is too improbable. Right now, dispite all that cash, the most popular service for code completion still has trouble working with a couple of easy words, regardless of them existing in every dictionary. There need to be a bug in the "totally free speech", or something.

But there is hope. Among the tricks of an approaching gamer to shock the marketplace, is to damage the incumbents by releasing their design free of charge, under a permissive license. This is what DeepSeek simply made with their DeepSeek-R1. Google did it earlier with the Gemma designs, as did Meta with Llama. We can download these designs ourselves and run them on our own hardware. Better yet, individuals can take these designs and scrub the predispositions from them. And we can download those scrubbed models and run those on our own hardware. And then we can lastly have some truly beneficial LLMs.

That hardware can be a difficulty, however. There are 2 choices to select from if you want to run an LLM in your area. You can get a big, powerful video card from Nvidia, or you can buy an Apple. Either is pricey. The main specification that shows how well an LLM will perform is the amount of memory available. VRAM when it comes to GPU's, typical RAM in the case of Apples. Bigger is better here. More RAM implies larger designs, which will dramatically enhance the quality of the output. Personally, I 'd say one needs 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 equipped to handle that can quickly cost countless euros.

So what to do, if you do not have that quantity of money to spare? You buy second-hand! This is a practical choice, however as constantly, there is no such thing as a totally free lunch. Memory might be the main issue, but do not undervalue the significance of memory bandwidth and other specs. Older devices will have lower performance on those elements. But let's not stress too much about that now. I am interested in developing something that at least can run the LLMs in a usable way. Sure, the most recent Nvidia card may do it much faster, but the point is to be able to do it at all. Powerful online models can be nice, however one must at the minimum have the alternative to change to a regional one, if the scenario requires it.

Below is my effort to build such a capable AI computer system without spending excessive. I wound 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 necessary to purchase a brand brand-new dummy GPU (see listed below), or I could have discovered somebody that would 3D print the cooling fan shroud for me, instead of delivering a ready-made one from a far country. I'll confess, morphomics.science I got a bit impatient at the end when I discovered I needed to buy yet another part to make this work. For me, this was an appropriate tradeoff.

Hardware

This is the full expense breakdown:

And this is what it looked liked when it first booted with all the parts set up:

I'll offer some context on the parts below, and after that, I'll run a few fast tests to get some numbers on the efficiency.

HP Z440 Workstation

The Z440 was a simple choice because I currently owned it. This was the starting point. About two years back, I desired a computer that might work 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 should work for hosting VMs. I bought it previously owned and after that switched the 512GB hard drive for a 6TB one to save those virtual machines. 6TB is not needed for running LLMs, and for that reason I did not include it in the breakdown. But if you plan to gather many models, 512GB might not be enough.

I have pertained to like this workstation. It feels all really solid, and I haven't had any problems with it. At least, till I began this project. It ends up that HP does not like competitors, and I encountered some difficulties when swapping elements.

2 x NVIDIA Tesla P40

This is the magic active ingredient. GPUs are expensive. But, similar to the HP Z440, frequently one can discover older devices, that utilized to be leading of the line and is still extremely capable, second-hand, for fairly little cash. These Teslas were implied to run in server farms, for things like 3D rendering and other graphic processing. They come equipped with 24GB of VRAM. Nice. They suit 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 indicated for servers. They will work great in the PCIe slots of a regular workstation, but in servers the cooling is handled in a different way. Beefy GPUs a great deal of power and can run extremely hot. That is the reason consumer GPUs always come geared up with big fans. The cards require to look after their own cooling. The Teslas, nevertheless, have no fans whatsoever. They get just as hot, however expect the server to supply a steady flow of air to cool them. The enclosure of the card is somewhat formed like a pipeline, and you have 2 options: blow in air from one side or blow it in from the other side. How is that for versatility? You absolutely must blow some air into it, though, or you will harm it as quickly as you put it to work.

The option is easy: just mount a fan on one end of the pipe. And certainly, it seems a whole home market has grown of individuals that sell 3D-printed shrouds that hold a basic 60mm fan in just the ideal location. The issue is, the cards themselves are already rather bulky, and it is challenging to find a configuration that fits two cards and 2 fan installs in the computer case. The seller who sold me my two Teslas was kind adequate to include two 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 annoying. The HP Z440 had a 700 Watt PSU, which may have sufficed. But I wasn't sure, and I needed to buy a new PSU anyway because it did not have the ideal connectors to power the Teslas. Using this convenient website, I deduced that 850 Watt would suffice, and I bought the NZXT C850. It is a modular PSU, indicating that you just require to plug in the cables that you actually need. It featured a cool bag to keep the extra cable televisions. One day, I might give it a great cleansing and utilize it as a toiletry bag.

Unfortunately, HP does not like things that are not HP, so they made it difficult to switch the PSU. It does not fit physically, and they likewise changed the main board and CPU adapters. All PSU's I have ever seen in my life are rectangle-shaped boxes. The HP PSU also is a rectangular box, but with a cutout, making certain that none of the typical PSUs will fit. For no technical reason at all. This is simply to mess with you.

The installing was eventually resolved by utilizing 2 random holes in the grill that I somehow handled to line up with the screw holes on the NZXT. It sort of hangs steady now, and I feel lucky that this worked. I have seen Youtube videos where people resorted to double-sided tape.

The adapter needed ... another purchase.

Not cool HP.

Gainward GT 1030

There is another problem with utilizing server GPUs in this consumer workstation. The Teslas are meant to crunch numbers, not to play computer game with. Consequently, they do not have any ports to link a monitor to. The BIOS of the HP Z440 does not like this. It declines to boot if there is no other way to output a video signal. This computer will run headless, wiki.myamens.com however we have no other choice. We have to get a 3rd video card, that we do not to intent to utilize ever, just to keep the BIOS delighted.

This can be the most scrappy card that you can find, naturally, however there is a requirement: we must make it fit on the main board. The Teslas are large 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 typically even when a GPU is marketed as x8, the actual connector on it may be just as broad as an x16. Electronically it is an x8, physically it is an x16. That won't work on this main board, we actually need the little adapter.

Nvidia Tesla Cooling Fan Kit

As said, the obstacle is to discover a fan shroud that suits the case. After some browsing, I found this set on Ebay a bought 2 of them. They came provided complete with a 40mm fan, and all of it fits completely.

Be alerted that they make an awful lot of noise. You don't wish to keep a computer with these fans under your desk.

To watch on the temperature, I whipped up this quick script and put it in a cron task. It regularly reads out the temperature on the GPUs and sends that to my Homeassistant server:

In Homeassistant I added a chart to the dashboard that displays the values in time:

As one can see, the fans were loud, but not especially efficient. 90 degrees is far too hot. I searched the web for a sensible upper limit however could not discover anything specific. The documents on the Nvidia site mentions a temperature of 47 degrees Celsius. But, what they mean by that is the temperature of the ambient air surrounding the GPU, not the determined worth on the chip. You know, the number that actually is reported. Thanks, Nvidia. That was practical.

After some further searching and checking out the opinions of my fellow web people, my guess is that things will be fine, provided that we keep it in the lower 70s. But do not estimate me on that.

My first attempt to treat the circumstance was by setting an optimum to the power usage of the GPUs. According to this Reddit thread, one can lower the power usage of the cards by 45% at the cost of only 15% of the performance. I attempted it and ... did not notice any difference at all. I wasn't sure about the drop in efficiency, having only a number of minutes of experience with this configuration at that point, however the temperature qualities were certainly unchanged.

And after that a light bulb flashed on in my head. You see, prior to 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 draws air into the case, and I figured this would operate 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 require any cooling. Looking into the BIOS, I found a setting for the minimum idle speed of the case fans. It ranged from 0 to 6 stars and was presently set to 0. Putting it at a greater setting did marvels for the temperature level. It likewise made more sound.

I'll hesitantly confess that the 3rd video card was handy when changing the BIOS setting.

MODDIY Main Power Adaptor Cable and Akasa Multifan Adaptor

Fortunately, sometimes things just work. These 2 items were plug and play. The MODDIY adaptor cable connected 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 good feature that it can power two fans with 12V and 2 with 5V. The latter certainly lowers the speed and hence the cooling power of the fan. But it also decreases sound. Fiddling a bit with this and the case fan setting, I discovered an appropriate tradeoff between sound and temperature level. For now at least. Maybe I will need to revisit this in the summer season.

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 models have the default quantization that ollama will pull for you if you do not specify anything.

Another important finding: Terry is without a doubt the most popular name for a tortoise, asteroidsathome.net followed by Turbo and Toby. Harry is a favorite for hares. All LLMs are loving alliteration.

Power usage

Over the days I watched 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 existing setup is to have actually 2 designs filled, one for coding, the other for generic text processing, and keep them on the GPU for as much as an hour after last usage.

After all that, gratisafhalen.be am I happy that I started this project? Yes, I think I am.

I spent a bit more money than planned, however I got what I wanted: a method of locally running medium-sized designs, entirely under my own control.

It was a good option to begin with the workstation I currently owned, and see how far I could come with that. If I had actually begun with a brand-new machine from scratch, it certainly would have cost me more. It would have taken me a lot longer too, as there would have been much more alternatives to select from. I would also have been extremely tempted to follow the buzz and purchase the current and biggest of everything. New and shiny toys are enjoyable. But if I purchase something brand-new, I want it to last for many years. Confidently predicting where AI will enter 5 years time is impossible today, so having a cheaper device, that will last a minimum of some while, feels satisfactory to me.

I want you best of luck on your own AI journey. I'll report back if I find something new or intriguing.