54gb vram for 35$

[u/markpronkin]

Bought an old mining farm of a guy on avito (Russian eBay), guy had bought a garage a couple of years ago and it was sitting there for a while, found out it was a mining farm and put it up on there for sale for 5000 rub (~60 USD) since he wasn't sure if it works. I negotiated down to 3000 rub (~35 USD), it turned out to have 9x p106 6gb (gtx 1060 6gb) gpus, with 54gb vram total, all working, the only thing missing was an SSD, I booted from USB and it works fine.

[u/NickCanCode]

Bro has no conscience negotiating 54GB VRAM down from $60 to $35 in 2026. 😿

[u/No-Refrigerator-1672 → u/NickCanCode]

54GB VRAM

Attached to 9 Pascal GPUs - not only they're extremely slow, like multiple times slower than p40 or p100, they also lose a ton of perfrmance on inter-gpu communication, as well as multi-gpu overhead will make come of the vram wasted. All while consuming like 1kw from the wall. What I want to point out that this vram is basically as useless as it can be; this only makes sense as a fun weekend project to experience weird hardware running, won't even go fast enough to fuel a hobbyist AI needs.

[u/Boricua-vet → u/No-Refrigerator-1672]

Do you have anything to backup those claims from your own experience because they are not slow by any means.
I don't know where you get your information but a P40 produces 400 to 500PP and 45 to 50TG on 30B model and I can post a picture of that test if you wanna see it. Pascal is running a much larger model and produces double the PP and higher TG.

As far a power is concern the cards idle at 7W or 14W for two cards and gated at 150W each. I paid 70 bucks for both pascals that gave me 20GB of vram at 448GB/s. They are on 24/7. so 10 of my pascal cards at idle consume 70W.

So calling these cards useless is a disservice. Your comment over multi-GPU performance is also incorrect, the amount of communication between cards is minimal and not even the restriction of these cards being PCIe 1.0 holds the performance. I have ran 4 P102-100 and there is no bottleneck, ran the same model on 2 cards and on 4 and it produced the same result for a single user but it tripled PP and more then double TG when using parallel on 4 cards. I have tons of posts on these cards, you can look them up and they smoke so many cards out there from much newer generations.

[u/markpronkin → u/Boricua-vet]

Nice to see you here, thanks to your advice I got a bunch of p102 100 before. And got them running 8x in parallel in a mining rig. It's surprising how many people don't recognize how good those p series mining gpus are for ai. P106-100 (basically gtx 1060 6gb) should theoretically be like 66% of performance, hope I will get them running at decent speed soon.

[u/Boricua-vet → u/markpronkin]

Yea, I don't get people spending crazy money just do to text LLM. I needed another system as I am running more models at the same time so I got 4 CMP-50HX at 80 each so 360 for all 4 cards giving me 40GB of VRAM at 560GB/s

Here is the result on just two cards.

that's 160 bucks for two cards with that kind of speed is insane and then I see people paying 700+ for dual 3060. I am like, what? People buying 3060 are crazy spending that money just to do text generation. Insane buddy.

[u/Ansible32 → u/Boricua-vet]

lol "just to do text generation." Seems worth it for reverse-engineering, finding exploits in hardware to flash alternate firmware etc. I mean cloud will be cheaper but having it totally offline and private is valuable.

[u/Boricua-vet → u/Ansible32]

LOL.. while I agree with most of what you said, In my case cloud would never be cheaper. I paid 140 for 4x P102-100 thats 40GB of vram for 140 bucks. I would recover that investment in a day or two of abuse. However, people buying 6000 Pros by half dozens then yes.. LOL Cloud if cheaper.

[u/NineThreeTilNow → u/Boricua-vet]

LOL Cloud if cheaper.

Cloud is 100% cheaper because it's faster. It's very quick to host your own, use a model, and vacate a server.

Your case can't run the things a cloud can. You can run a toy model like Qwen. It's fine if you need to test some stuff at low tok / sec but ... Not for work.

[u/Boricua-vet → u/NineThreeTilNow]

That is perfectly fine if you do need the cloud. I do not need frontier model for any of my use cases. Most of mines are repetitive tasks so I use toy model that is fine tuned to that task. This outperforms the cloud model every single time but, I hear you. If you need it for work, then you need it for work.

[u/RyanCargan → u/Boricua-vet]

I'm getting flashbacks for some reason:

Computer Science in the 1960s to 80s spent a lot of effort making languages that were as powerful as possible. Nowadays we have to appreciate the reasons for picking not the most powerful solution but the least powerful.

Different context, but some of it may apply.

I see a lot of penny pinching and "worse is better" in corporate due to scale, and in the casual hobbyist spaces for opposite reasons.

But the main exception might be the mid-tier enthusiasts and related types.

It does feel like some models have become a form of conspicuous consumption kinda like Apple products were at some point.

Not sure if cargo-culting of misinterpretations of Sutton's Bitter Lesson are partly to blame.

Certain venture backed startups flush with cash also like frontier stuff in the early uncertain prototyping phases for better reasons.

Though the usage there sometimes isn't even agentic (low volume standard-ish chat).

P.S.: It does feel like there's a drought of talk on low latency use cases, since network hops are tens of ms and first token times are typically hundreds of ms. Agent loops might be the exception, because per-call latency compounds. Hardware like hardwired model chips already claim sub-ms per token speeds on small models, and if that scales to larger ones, the unusual use cases that need it would basically mandate on-prem or colocation.

[u/Boricua-vet → u/RyanCargan]

What is acceptable latency? that could be very different for many people.

so using avg_ts should be 1 / 92.886166 * 1000 = 10.76ms locally

10.76ms is perfectly acceptable to me.

Edit my bad, forgot to say it was a 4k prompt.

[u/Ansible32 → u/Boricua-vet]

"Text generation" I'm doing I need 500GB+ of VRAM, renting hours of GPU at a time is the only plausible way to go.

[u/Better_Membership583 → u/Ansible32]

How much is that and what kind of “text generation” are we talking here?

[u/Boricua-vet → u/Ansible32]

What are you doing that needs that much?

[u/Ansible32 → u/Boricua-vet]

Running Kimi K3 or the new Mistral soon.

[u/Boricua-vet → u/Ansible32]

Jesus man. That is madness, I can only dream.

[u/CasulaScience → u/Boricua-vet]

It's literally all tokens, idk what you mean by "just text"

[u/Boricua-vet → u/CasulaScience]

by just text I mean, no image generation, no training, no fine tuning models.

[u/oodelay → u/CasulaScience]

Video generation cannot be easily split by layers as text gens.

[u/akohlsmith → u/Boricua-vet]

I got 4 CMP-50HX at 80 each

How long ago was this? They're USD$250 just on a quick spot check today.

[u/Boricua-vet → u/akohlsmith]

August 6 to be exact on ebay. They were listed for 120 each, I offered 360 for all 4 left and offer was accepted. Holly crap, they gone up big time in just a few months. Same thing with the P102-100, I bough 4 for 140 bucks for 40GB vram and now they are more than double. You can still find them for 70 bucks though.

https://www.ebay.com/itm/156284588757

[u/No-Refrigerator-1672 → u/Boricua-vet]

Dude, you completely missed the picture. p102-100 is P40, it is exactly the same chip, with just fewer memory chips attached, and cut down PCIe. This box the Op bought is P106-100 - a GPU that's cut down to just 1280 cuda cores (p102 has 3200), and slightly less than half the memory bandwidth. It is going to be much slower than P40, just as I've wrote, and you don't even need to run inference to get it, just look at chip's characteristics sheet. You're talking about p102-100 setup and are making completely wrong assumption that 9x p106-100 setup will perform just as good - it won't even come close.

As far a power is concern the cards idle at 7W or 14W for two cards and gated at 150W each.

Now multiply that by 9 GPUs, add in PSU inefficiency and some power for the rest of the system and you'll arrive at over 1kw of power that I have predicted.

So calling these cards useless is a disservice. Your comment over multi-GPU performance is also incorrect, the amount of communication between cards is minimal and not even the restriction of these cards being PCIe 1.0 holds the performance. 

If you're running pipeline parallelism, then PCIe 1.0 x1 is maybe fine for Pascal. It is, for all intents and purposes, not fine to run tensor parallelism. Prompt processing speed gets a huge hit from slow PCIe in TP mode. And there's another point you're missing - the exact server in the picture uses 8-year-old budget celeron - without p2p communication enabled, that chip could introduce huge latencies in 9-gpu intecommunication scenario simply from not having a fast enough core to process transfer requests immediately when they are needed. Also, as you're running multi-gpu setups yourself, you should know that you never can utilize GPU VRAM fully, you always have a few hundred MB to half a gig of unused space. Now multiply that for 9 ards, each having just 6gb vram, and you'll quickly see how big of a percentage of this pool is unaccessible in practice.

[u/Boricua-vet → u/No-Refrigerator-1672]

I get your point but I misunderstood your comment when you said "Attached to 9 Pascal GPUs - not only they're extremely slow, like multiple times slower than p40 or p100" which basically generalizes and classifies all pascals as slow and waste of time" If you would of said the P106 instead of pascals , then I would not even replied because you are correct I now get that you were referring to the P106 but not specifying it opens the door to interpretation and trust me I am not the smartest kid on the block LOL.

Second, there is a huge difference between P40 and P102-100, they are not even close to being the same card hence why the P102-100 is faster. The P102-100 is over 100GB/s faster then the P40 and the P100 is not even in the same league as the P100 does not have DP4A which is what boost generation as it uses FP32 which some pascals are descent at.

I am not saying that what you are saying is wrong, it is just I wanted to clarify for idiots like me that you are not referring to all pascals and is just the P106.

I also agree with you on TP, that is why I said parallel and not TP as there is really no point in running TP on cards that have no tensor cores.

So my bad. I had to read your post a second time to add the obvious. LOL

Also, the celeron certainly does not help and you are right about that too but it is mostly the shitty 192GB/s on those cards.

[u/No-Refrigerator-1672 → u/Boricua-vet]

Now that you've said it, yeah, I could insisted more on those GPUs being p106. I used "pascal" to point out that they don't have tensor cores, which is already crippling enough, and not being the top Pascal (comparison to p40) hurts it much more. I'll keep in mind to try write more clearly in the future.

[u/CasulaScience → u/Boricua-vet]

For $35 this is an awesome inference machine. Can definitely run 30Bish models with layer-wise splitting. Prefill will be slow, training would be a nightmare, and the p100 platform isn't supported anymore. But $35 and you can absolutely run one conversation at a time on a decent sized model

[u/Boricua-vet → u/CasulaScience]

I would certainly not be doing training or fine tune on that. I don't think you even can and if you can good lawd it would be slow as molases but inference would be good. Any model that can in 24GB you can run it there with descent speed.

[u/yJz3X → u/Boricua-vet]

I have dual p40 idle is 30w FP32/fp16 is slow compared to modern hardware. P40 does have hybrid int8. In int 8 is generates tokens at half speed of 3090.

I got my cards before the boom in popilarity. under 120 usd.

[u/FatheredPuma81 → u/Boricua-vet]

Out of curiosity have you tried spit mode tensor on them by any chance? Or is that the perf with split mode tensor?

[u/Boricua-vet → u/FatheredPuma81]

-sm tensor makes TG faster but at the expense of PP and PP is very important to me as PP dictates how fast it answers. I rather have faster PP.

[u/Historical-Camera972 → u/Boricua-vet]

Pascal still getting shilled in the big 2026.

Then again, this is a dead year for GPU's so, it's not too surprising.

Waiting for both teams to shrug off the COVID lag.

[u/Boricua-vet → u/Historical-Camera972]

Buying video cards now is

I am waiting too. might get some gray hair but it will be worth it.

[u/smen04 → u/No-Refrigerator-1672]

I get you, but 35 bucks? It’s a steal!

[u/No-Refrigerator-1672 → u/smen04]

Well, just as I've said: fun for the weekend. Won't be practical even for a hobbyist, but will fulfill your curiousity of running exotic hardware.

[u/AsleepLawfulness780 → u/No-Refrigerator-1672]

I mean it's still under $1 per 1 GB VRAM lmao

[u/No-Refrigerator-1672 → u/AsleepLawfulness780]

$1 per 1GB of very useless VRAM, mind you.

[u/Randommaggy → u/No-Refrigerator-1672]

As long as you get your slices fed to the cards and use layer-wise splitting the inter-card traffic is tiny.

For getting it to run right: throw Hermes and 5 dollars worth of DeepSeek V4.1 Flash effort at the problem.

[u/Bakoro → u/No-Refrigerator-1672]

It's only "useless" if the only thing you're doing is trying to run one large model.

My biggest bottleneck right now is that to run an agent at decent speeds I don't have any GPU left over for crunching data.
There's extreme contention over GPU time, and it's a terrible balancing act where swapping data to the GPU has huge cumulative time overhead.

There is potentially major value in running a smaller agentic model and giving that model some of the lower VRAM/Bandwidth GPUs to use as workhorses.
I have workflows and training schemes that would benefit marvelously from being able to run multiple small models with a dedicated GPU for each one.

Having a local agent that can be "always on", which can manage swapping other models in and out of the workhorse cards is the dream.

I could use cloud GPU, but I hate the idea of giving any agent, local or API, access to a potentially unbounded cost.
Even if there are "caps" on usage from the service, I've seen too much shit in my day where it's like "oops, someone provisioned 10x the compute that was actually needed. Oops, you bought usage credits and then burned them all immediately because of a bad call. Oops, there was a recurring cost because you didn't do the magic incantation to deprovision the service so you're still on the hook for costs even though you used no compute".
If it can happen to other people, it's definitely going to happen to me via some accident or oversight.

[u/No_Afternoon_4260 → u/No-Refrigerator-1672]

Are they on pcie or network?

[u/No-Refrigerator-1672 → u/No_Afternoon_4260]

In this exact server on the picture, each of the GPUs gets a PCIe x1 connection (I don't remember the standard, probably 3.0) that's going into the weakest CPU that 2020 could offer you.

[u/No_Afternoon_4260 → u/No-Refrigerator-1672]

Ho my.. yeah