As everybody is well conscious, the world is still going nuts trying to establish more, newer and better AI tools. Mainly by throwing absurd amounts of money at the problem. Much of those billions go towards building cheap or complimentary services that operate at a significant loss. The tech giants that run them all are intending to draw in as numerous users as possible, so that they can capture the marketplace, and become the dominant or only celebration that can use them. It is the classic Silicon Valley playbook. Once dominance is reached, expect the enshittification to begin.

A most likely way to make back all that money for establishing these LLMs will be by tweaking their outputs to the taste 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 happened at Tiananmen Square in 1989. That a person is certainly politically encouraged, however ad-funded services won't exactly be enjoyable either. In the future, I fully expect to be able to have a frank and truthful conversation about the Tiananmen occasions with an American AI representative, but the just one I can afford will have assumed the persona of Father Christmas who, while holding a can of Coca-Cola, will intersperse the stating of the tragic events with a joyful "Ho ho ho ... Didn't you know? The holidays are coming!"

Or perhaps that is too far-fetched. Right now, dispite all that cash, the most popular service for code conclusion still has problem dealing with a couple of easy words, regardless of them being present in every dictionary. There should be a bug in the "free speech", or something.

But there is hope. Among the tricks of an approaching player to shock the market, is to undercut the incumbents by releasing their design free of charge, under a permissive license. This is what DeepSeek just made 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, individuals can take these designs and scrub the biases from them. And we can download those scrubbed models and run those on our own hardware. And after that we can finally have some truly helpful LLMs.

That hardware can be a hurdle, however. There are 2 alternatives to select from if you want 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 pricey. The main specification that indicates how well an LLM will perform is the quantity of memory available. VRAM in the case of GPU's, normal RAM in the case of Apples. Bigger is better here. More RAM means larger designs, which will significantly improve the quality of the output. Personally, I 'd say one requires at least over 24GB to be able to run anything helpful. That will fit a 32 billion criterion model with a little headroom to spare. Building, or buying, a workstation that is equipped to handle that can quickly cost countless euros.

So what to do, if you don't have that quantity of money to spare? You purchase pre-owned! This is a practical 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 devices will have lower efficiency on those aspects. But let's not worry excessive about that now. I am interested in constructing something that at least can run the LLMs in a functional way. Sure, the most recent Nvidia card may do it faster, but the point is to be able to do it at all. Powerful online designs can be great, however one need to at least have the choice to switch to a local one, if the situation requires it.

Below is my attempt to build such a capable AI computer system without investing too much. I wound up with a workstation with 48GB of VRAM that cost me around 1700 euros. I might have done it for less. For instance, it was not strictly needed to purchase a brand new dummy GPU (see listed below), or I could have found somebody that would 3D print the cooling fan shroud for me, rather of shipping a ready-made one from a far nation. I'll admit, I got a bit restless at the end when I learnt I had to purchase 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 installed:

I'll provide some context on the parts listed below, and after that, I'll run a few quick tests to get some numbers on the performance.

HP Z440 Workstation

The Z440 was an easy choice because I already owned it. This was the starting point. About 2 years earlier, I wanted a computer that might work as a host for my virtual machines. 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 purchased it secondhand and then swapped the 512GB hard disk drive for a 6TB one to keep those virtual devices. 6TB is not required for running LLMs, and therefore I did not include it in the breakdown. But if you plan to gather numerous designs, 512GB might not suffice.

I have actually pertained to like this workstation. It feels all really solid, and I haven't had any issues with it. A minimum of, until I started this project. It ends up that HP does not like competition, and I came across some difficulties when swapping parts.

2 x NVIDIA Tesla P40

This is the magic ingredient. GPUs are expensive. But, as with the HP Z440, frequently one can find older equipment, that utilized to be leading of the line and is still very capable, second-hand, for fairly little cash. These Teslas were suggested to run in server farms, for things like 3D making and other graphic processing. They come geared up with 24GB of VRAM. Nice. They suit a PCI-Express 3.0 x16 slot. The Z440 has two of those, so we purchase two. Now we have 48GB of VRAM. Double good.

The catch is the part about that they were meant for servers. They will work great in the PCIe slots of a regular workstation, however in servers the cooling is handled in a different way. Beefy GPUs consume a lot of power and can run really hot. That is the reason customer GPUs always come geared up with big fans. The cards require 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 consistent circulation of air to cool them. The enclosure of the card is somewhat shaped 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 need to blow some air into it, though, or you will harm it as quickly as you put it to work.

The service is easy: simply mount a fan on one end of the pipeline. And certainly, it seems a whole cottage industry has actually grown of people that sell 3D-printed shrouds that hold a basic 60mm fan in simply the ideal location. The issue is, the cards themselves are already rather bulky, and it is difficult to discover a configuration that fits two cards and two fan mounts in the computer case. The seller who sold me my two Teslas was kind enough to consist of 2 fans with shrouds, however there was no chance I might 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 might have been enough. But I wasn't sure, and I needed to buy a new PSU anyway due to the fact that it did not have the best connectors to power the Teslas. Using this convenient website, I deduced that 850 Watt would suffice, and I purchased the NZXT C850. It is a modular PSU, implying that you only require to plug in the cables that you really require. It featured a neat bag to save the spare cables. One day, I may give it a great cleansing and use it as a toiletry bag.

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

The mounting was eventually solved 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 stable now, and I feel lucky that this worked. I have seen Youtube videos where people resorted to .

The adapter required ... another purchase.

Not cool HP.

Gainward GT 1030

There is another concern with utilizing server GPUs in this customer workstation. The Teslas are intended to crunch numbers, not to play computer game with. Consequently, they do not have any ports to link a display to. The BIOS of the HP Z440 does not like this. It refuses to boot if there is no way to output a video signal. This computer will run headless, but we have no other option. We need to get a third video card, that we do not to intent to utilize ever, simply to keep the BIOS pleased.

This can be the most scrappy card that you can find, obviously, however there is a requirement: we should make it fit on the main board. The Teslas are large and fill the two 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 site for some background on what those names mean. One can not buy any x8 card, however, because often even when a GPU is advertised as x8, the actual adapter on it may be simply as broad as an x16. Electronically it is an x8, physically it is an x16. That will not work on this main board, we really require the small connector.

Nvidia Tesla Cooling Fan Kit

As said, the challenge is to find a fan shroud that suits the case. After some searching, I discovered this kit on Ebay a purchased 2 of them. They came delivered total with a 40mm fan, and all of it fits completely.

Be warned that they make a dreadful great deal of noise. You do not desire to keep a computer with these fans under your desk.

To keep an eye on the temperature level, I worked up this quick script and put it in a cron job. It regularly reads out the temperature on the GPUs and sends out that to my Homeassistant server:

In Homeassistant I added a chart to the control panel that displays the values gradually:

As one can see, the fans were noisy, but not especially effective. 90 degrees is far too hot. I browsed the internet for an affordable ceiling but could not find anything specific. The paperwork on the Nvidia website points out a temperature level of 47 degrees Celsius. But, what they mean by that is the temperature of the ambient air surrounding the GPU, not the determined value on the chip. You understand, the number that actually is reported. Thanks, Nvidia. That was handy.

After some more searching and reading the opinions of my fellow web people, my guess is that things will be great, supplied that we keep it in the lower 70s. But do not estimate me on that.

My very first effort to fix the scenario was by setting a maximum to the power usage of the GPUs. According to this Reddit thread, one can reduce the power consumption of the cards by 45% at the expense of only 15% of the performance. I tried it and ... did not discover any distinction at all. I wasn't sure about the drop in efficiency, having only a couple of minutes of experience with this configuration at that point, but the temperature level attributes were certainly the same.

And after that a light bulb flashed on in my head. You see, just before the GPU fans, there is a fan in the HP Z440 case. In the image 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 operate in tandem with the GPU fans that blow air into the Teslas. But this case fan was not spinning at all, because 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 currently set to 0. Putting it at a greater setting did wonders for the temperature level. It also made more noise.

I'll unwillingly admit that the third video card was practical when changing the BIOS setting.

MODDIY Main Power Adaptor Cable and Akasa Multifan Adaptor

Fortunately, often things just work. These 2 products were plug and play. The MODDIY adaptor cable television connected the PSU to the main board and CPU power sockets.

I utilized the Akasa to power the GPU fans from a 4-pin Molex. It has the great function that it can power two fans with 12V and two with 5V. The latter certainly minimizes the speed and therefore the cooling power of the fan. But it also reduces noise. Fiddling a bit with this and the case fan setting, I found an acceptable tradeoff between noise and akropolistravel.com temperature level. In the meantime at least. Maybe I will require to revisit this in the summer.

Some numbers

Inference speed. I gathered these numbers by running ollama with the-- verbose flag and asking it five times to compose a story and averaging the result:

Performancewise, ollama is configured with:

All designs have the default quantization that ollama will pull for you if you don't specify anything.

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

Power intake

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 model on the card enhances latency, however takes in more power. My existing setup is to have two models 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 began this project? Yes, I think I am.

I invested a bit more cash than planned, but I got what I desired: a way of locally running medium-sized models, totally under my own control.

It was a great choice to start with the workstation I currently owned, and see how far I might come with that. If I had begun with a brand-new device from scratch, it certainly would have cost me more. It would have taken me a lot longer too, as there would have been a lot more choices to pick from. I would also have actually been very tempted to follow the hype and buy the current and greatest of everything. New and glossy toys are fun. But if I purchase something new, I want it to last for several years. Confidently forecasting 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 acceptable to me.

I wish you best of luck by yourself AI journey. I'll report back if I discover something brand-new or fascinating.