How is that For Flexibility?

As everyone is well mindful, the world is still going nuts attempting to develop more, newer and much better AI tools. Mainly by throwing absurd amounts of money at the problem. Many of those billions go towards developing inexpensive or free services that run at a substantial loss. The tech giants that run them all are wishing to draw in as numerous users as possible, so that they can record the market, and become the dominant or only party that can use them. It is the timeless Silicon Valley playbook. Once dominance is reached, expect the enshittification to start.

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 most. An example of what that such tweaking appears like is the refusal of DeepSeek’s R1 to discuss what happened at Tiananmen Square in 1989. That a person is certainly politically motivated, but ad-funded services will not precisely be enjoyable either. In the future, I completely expect to be able to have a frank and sincere conversation about the Tiananmen events with an American AI agent, however the only one I can afford will have assumed the persona of Father Christmas who, while holding a can of Coca-Cola, will intersperse the recounting of the terrible events with a joyful „Ho ho ho … Didn’t you understand? The vacations are coming!“

Or possibly that is too . Today, dispite all that cash, the most popular service for code conclusion still has problem dealing with a couple of easy words, in spite of them being present in every dictionary. There need to be a bug in the „free speech“, or something.

But there is hope. One of the tricks of an upcoming player to shock the market, is to damage the incumbents by launching their model for complimentary, under a liberal license. This is what DeepSeek just finished with their DeepSeek-R1. Google did it earlier with the Gemma models, 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 then we can finally have some really beneficial LLMs.

That hardware can be a hurdle, however. There are two options to pick from if you wish to run an LLM in your area. You can get a huge, powerful video card from Nvidia, or you can purchase an Apple. Either is costly. The main spec that suggests 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 models, which will significantly improve the quality of the output. Personally, I ‘d say one needs a minimum of over 24GB to be able to run anything useful. That will fit a 32 billion specification design with a little headroom to spare. Building, or purchasing, a workstation that is equipped to manage that can quickly cost countless euros.

So what to do, if you do not have that amount of cash to spare? You purchase second-hand! This is a feasible choice, but as constantly, there is no such thing as a complimentary lunch. Memory may be the main issue, but don’t ignore the significance of memory bandwidth and other specs. Older devices will have lower efficiency on those aspects. 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 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 models can be great, however one ought to at least have the alternative to switch to a local one, if the scenario requires it.

Below is my attempt to construct such a capable AI computer without investing too much. I wound up with a workstation with 48GB of VRAM that cost me around 1700 euros. I could have done it for less. For example, it was not strictly necessary to purchase a brand new dummy GPU (see below), or I could have found somebody that would 3D print the cooling fan shroud for me, instead of shipping a ready-made one from a faraway country. I’ll confess, I got a bit impatient at the end when I discovered out I needed to purchase 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 initially booted up with all the parts set up:

I’ll give some context on the parts below, and after that, I’ll run a couple of fast tests to get some numbers on the performance.

HP Z440 Workstation

The Z440 was a simple pick since I currently owned it. This was the beginning point. About 2 years back, I desired a computer system that might act as a host for my virtual devices. 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 purchased it secondhand and then swapped the 512GB hard disk for a 6TB one to store 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 collect lots of designs, 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, up until I began this task. It turns out that HP does not like competition, and photorum.eclat-mauve.fr I experienced some difficulties when switching components.

2 x NVIDIA Tesla P40

This is the magic active ingredient. GPUs are pricey. But, similar to the HP Z440, often one can discover older equipment, that used to be top of the line and is still extremely capable, pre-owned, for fairly little cash. These Teslas were indicated 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 2 of those, so we purchase 2. Now we have 48GB of VRAM. Double great.

The catch is the part about that they were indicated for servers. They will work fine in the PCIe slots of a typical workstation, however in servers the cooling is handled differently. Beefy GPUs take in a lot of power and can run very hot. That is the reason consumer GPUs always come equipped with huge fans. The cards need to take care of their own cooling. The Teslas, nevertheless, have no fans whatsoever. They get simply as hot, however anticipate the server to provide a steady circulation of air to cool them. The enclosure of the card is rather shaped like a pipe, and you have two choices: blow in air from one side or blow it in from the opposite. How is that for versatility? You absolutely must blow some air into it, however, or you will harm it as soon as you put it to work.

The option is easy: just mount a fan on one end of the pipeline. And certainly, it seems a whole home market has actually grown of individuals that sell 3D-printed shrouds that hold a standard 60mm fan in just the best location. The issue is, the cards themselves are currently quite bulky, and it is difficult to discover a setup that fits 2 cards and two fan installs in the computer system case. The seller who sold me my 2 Teslas was kind adequate 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 buy 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 brand-new PSU anyway since it did not have the ideal adapters 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, suggesting that you only need to plug in the cables that you in fact require. It featured a neat bag to store the spare cables. One day, I might provide it an excellent 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 altered the main board and CPU connectors. 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 reason at all. This is simply to mess with you.

The mounting was eventually resolved by using two 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 actually seen Youtube videos where individuals turned to double-sided tape.

The port needed … another purchase.

Not cool HP.

Gainward GT 1030

There is another issue with utilizing server GPUs in this consumer workstation. The Teslas are intended to crunch numbers, not to play video games 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 method to output a video signal. This computer system will run headless, but we have no other choice. We need to get a 3rd 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, but there is a requirement: we should 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 site for some background on what those names suggest. One can not buy any x8 card, however, because typically even when a GPU is promoted as x8, the real port on it might be just as wide as an x16. Electronically it is an x8, physically it is an x16. That will not deal with this main board, we really need the little adapter.

Nvidia Tesla Cooling Fan Kit

As said, the challenge is to discover a fan shroud that fits in the case. After some searching, I discovered this kit on Ebay a bought 2 of them. They came provided total with a 40mm fan, and it all fits perfectly.

Be cautioned that they make a terrible lot of sound. You don’t want to keep a computer with these fans under your desk.

To watch on the temperature level, I worked up this fast script and put it in a cron job. 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 control panel that displays the worths in time:

As one can see, the fans were loud, however not especially efficient. 90 degrees is far too hot. I browsed the internet for a reasonable ceiling however could not find anything specific. The paperwork on the Nvidia website discusses 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 determined worth on the chip. You understand, the number that in fact is reported. Thanks, Nvidia. That was valuable.

After some further browsing and reading the viewpoints of my fellow web citizens, my guess is that things will be fine, offered that we keep it in the lower 70s. But don’t estimate 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 decrease the power intake of the cards by 45% at the expense of just 15% of the performance. I attempted it and … did not see any difference at all. I wasn’t sure about the drop in efficiency, having only a number of minutes of experience with this setup at that point, however the temperature characteristics were certainly the same.

And then 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 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 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 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 higher setting did wonders for the temperature. It also made more sound.

I’ll reluctantly confess that the 3rd video card was useful when adjusting the BIOS setting.

MODDIY Main Power Adaptor Cable and Akasa Multifan Adaptor

Fortunately, in some cases things just work. These two products were plug and play. The MODDIY adaptor cable 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 nice function that it can power two fans with 12V and two with 5V. The latter certainly lowers the speed and thus the cooling power of the fan. But it likewise reduces noise. Fiddling a bit with this and the case fan setting, I found an acceptable tradeoff between noise and temperature. In the meantime a minimum of. Maybe I will need to revisit 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 balancing 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 favorite for hares. All LLMs are caring alliteration.

Power intake

Over the days I watched on the power intake 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, however consumes more power. My current setup is to have two models filled, one for coding, the other for generic text processing, and keep them on the GPU for up to an hour after last usage.

After all that, am I delighted that I started this task? Yes, wiki.eqoarevival.com I believe I am.

I spent a bit more cash than prepared, however I got what I wanted: a method of in your area running medium-sized designs, completely under my own control.

It was an excellent option to begin with the workstation I already owned, and see how far I might feature that. If I had begun 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 lots of more choices to select from. I would also have actually been extremely tempted to follow the buzz and purchase the most recent and biggest of whatever. New and glossy toys are enjoyable. But if I purchase something brand-new, I desire it to last for many years. Confidently predicting where AI will enter 5 years time is difficult today, so having a more affordable maker, that will last at least some while, feels acceptable to me.

I want you great luck on your own AI journey. I’ll report back if I find something brand-new or intriguing.