Building a deep learning rig

https://samsja.github.io/blogs/rig/part_1/

by dvcoolarun • 2 years ago

164 106 2 years ago

samsja.github.io

infogulch2• 2 years ago

I'm eyeing Tinybox as a deep learning rig.

https://tinygrad.org/

https://twitter.com/__tinygrad__/status/1760988080754856210

Smith42• 2 years ago

$15k!

KeplerBoy3• 2 years ago

Which is not unreasonable for that amount of hardware.

You have to ask yourself if you want to drop that kind of money on consumer GPUs, which launched late 2022. But then again, with that kind of money you are stuck with consumer GPUs either way, unless you want to buy Ada workstation cards for 6k each and those are just 4090s with p2p memory enabled. Hardly worth the premium, if you don't absolutely have to have that.

treprinum• 2 years ago

Sure, if you want to waste your time on getting stuff working on AMD instead of spending it on actual model training...

kkielhofner6• 2 years ago

This.

People complain about the "Nvidia tax". I don't like monopolies and I fully support the efforts of AMD, Intel, Apple, anyone to chip away at this.

That said as-is with ROCm you will:

- Absolutely burn hours/days/weeks getting many (most?) things to work at all. If you get it working you need to essentially "freeze" the configuration because an upgrade means do it all over again.

- In the event you get it to work at all you'll realize performance is nowhere near the hardware specs.

- Throw up your hands and go back to CUDA.

Between what it takes to get ROCm to work and the performance issues the Nvidia tax becomes a dividend nearly instantly once you factor in human time, less-than-optimal performance, and opportunity cost.

Nvidia says roughly 30% of their costs are on software. That's what you need to do to deliver something that's actually usable in the real world. With the "Nvidia tax" they're also reaping the benefit of the ~15 years they've been sinking resources into CUDA.

dkjaudyeqooe3• 2 years ago

Resistance is useless! Lets just accept our fate and toe the line. Why feel bad about paying essentially double, or getting half the compute for our money when we can just choose the easy route and accept our fate and feed the monopoly a little more money so they can charge us even more money. Who needs competition!

lbotos• 2 years ago

Isn't the entire point of tinygrad and the tinybox the "apple style" of we are building this software to work best on our hardware?

tutfbhuf2• 2 years ago

This is the new startup from George Hotz. I would like him to succeed, but I'm not so optimistic about their chances of selling a $15k box that is most likely less than $10k in parts. Most people would do much better by buying a second-hand 3090 or similar and connecting them into a rig.

segmondy7• 2 years ago

Not necessarily, I'm not sure about AMD GPUs, but he tweeted that AMD supports linking all 6 together. If that's the case, then 6 of those XTX should crush 6 3090's. For us techies we definitely will decide to build vs buy. However businesses would definitely decide to buy vs build.

downrightmike2• 2 years ago

He keeps hopping from thing to thing

lostmsu2• 2 years ago

As I mentioned in comments to this post on twitter, you can beat this with a pretty regular $6000 2x4090 system on compute (but with less total VRAM).

abra03• 2 years ago

I was thinking of doing something similar, but I am a bit sceptical about how the economics on this works out. On vast.ai renting a 3x3090 rig is $0.6/hour. The electricity price of operating this in e.g. Germany is somewhere about $0.05/hour. If the OP paid 1700 EUR for the cards, the breakeven point would be around (haha) 3090 hours in, or ~128 days, assuming non-stop usage. It's probably cool to do that if you have a specific goal in mind, but to tinker around with LLMs and for unfocused exploration I'd advise folks to just rent.

cyanydeez4• 2 years ago

the current economics is a low ball to get costumers. it's absolutely not going to be the market price once commercial interests have locked in their products.

but if you're just goofing around and not planning to create anything production worthy, it's a great deal.

whimsicalism8• 2 years ago

the current economics is a low ball to get costumers.

vast.ai is basically a clearinghouse. they are not doing some VC subsidy thing

in general, community clouds are not suitable for commercial use.

imiric4• 2 years ago

On vast.ai renting a 3x3090 rig is $0.6/hour. The electricity price of operating this in e.g. Germany is somewhere about $0.05/hour.

Are you factoring in the varying power usage in that electricity price?

The electricity cost of operating locally will vary depending on the actual system usage. When idle, it should be much cheaper. Whereas in cloud hosts you pay the same price whether the system is in use or not.

Plus with cloud hosts reliability is not guaranteed. Especially with vast.ai, where you're renting other people's home infrastructure. You might get good bandwidth and availability on one host, but when that host disappears, you should hope that you did a backup, which vast.ai charges for separately, and if so, you need to spend time restoring the backup to another, hopefully equally reliable host, which can take hours depending on the amount of data and bandwidth.

I recently built an AI rig and went with 2x3090s, and am very happy with the setup. I evaluated vast.ai beforehand, and my local experience is much better, while my electricity bill is not much higher (also in EU).

KeplerBoy3• 2 years ago

Well rented cloud instances shouldn't idle in the first place.

abra03• 2 years ago

Well if you are not using a rented machine during a period of time, you should release it.

Agreed on reliability and data transfer, that's a good point.

Out of curiosity, what do you use a 2x3090 rig for? Bulk not time-sensitive inference on down quanted models?

whimsicalism8• 2 years ago

with runpod/vast, you can request a set amount of time - generally if I request from Western EU or North America the availability is fine on the week-to-month timescale.

fwiw I find runpod's vast clone significantly better than vast and there isn't really a price premium.

algo_trader2• 2 years ago

built an AI rig and went with 2x3090s,

Is there a goto card for low memory (1-2BN) models?

Something with much better flops/$ but purposely crippled with low memory.

mirekrusin• 2 years ago

For me "economics" are:

- if I have it locally, I'll play with it

- if not, I won't (especially with my data)

- if I have something ready for a long run I may or may not want to send it somewhere (it's not going to be on 3090s for sure if I send it)

- if I have requirement to have something public I'd probably go for per usage with ie [0].

[0] https://www.runpod.io/serverless-gpu

kkielhofner6• 2 years ago

With the current more-or-less dependency on CUDA and thus Nvidia hardware it's about making sure you actually have the hardware available consistently.

I've had VERY hit-miss results with Vast.ai and I'm convinced people are cheating their evaluation stuff because when the rubber meets the road it's very clear performance isn't what it's claimed to be. Then you still need to be able to actually get them...

whimsicalism8• 2 years ago

use runpod and yeah i think vast.ai has some scams, especially in the asian and eastern european nodes.

KuriousCat3• 2 years ago

When you compute the break even point did you factor in that you still own the cards and you can resell them? I bought my 3090s for 1000$ and after 1 year I think they go for more in the open market if I resell them now.

wiradikusuma• 2 years ago

For me the economics is when I'm not using it to do AI stuff, I can use it to play games with max settings.

Unfortunately my CFO (a.k.a Wife) does not share the same understanding.

ejb999• 2 years ago

I fear that someday I will die and my wife will sell off all my stuff for what I said I paid for it.

(not really, but it is a joke I read someplace and I think it applies to a lot of couples).

verticalscaler• 2 years ago

Well maybe you could rent it out to others for 256 days at $0.3/hour, tinker, and sell it for parts after you get bored with it. ;)

ametrau• 2 years ago

Interesting. I checked it out. The providers running your docker container have access to all your data.

Luc• 2 years ago

Breakeven point would be less than 128 days due to the (depreciating) resale value of the rig.

segmondy7• 2 years ago

Well, almost. GPUs have not be depreciating. The cost of 3090's and 4090's have gone up. Folks are selling it for what they paid for or even more. With the recent 40's SUPER series from Nvidia, I'm not expecting any new releases in a year. AMD & Intel still have ways to go before major adoption. Startups are buying up consumer cards. So I sadly expect prices to stay more or less the same.

segmondy7• 2 years ago

Unless you are training, you never hit peak watts. When inferring, the watt is still minimal. I'm running inference now and using 20%. GPU 0 is using more because I have it as main GPU. Idle watt sits at about 5%.

Device 0 [NVIDIA GeForce RTX 3060] PCIe GEN 3@16x RX: 0.000 KiB/s TX: 55.66 MiB/s GPU 1837MHz MEM 7300MHz TEMP 43°C FAN 0% POW 43 / 170 W GPU[|| 5%] MEM[|||||||||||||||||||9.769Gi/12.000Gi]

Device 1 [Tesla P40] PCIe GEN 3@16x RX: 977.5 MiB/s TX: 52.73 MiB/s GPU 1303MHz MEM 3615MHz TEMP 22°C FAN N/A% POW 50 / 250 W GPU[||| 9%] MEM[||||||||||||||||||18.888Gi/24.000Gi]

Device 2 [Tesla P40] PCIe GEN 3@16x RX: 164.1 MiB/s TX: 310.5 MiB/s GPU 1303MHz MEM 3615MHz TEMP 32°C FAN N/A% POW 48 / 250 W GPU[|||| 11%] MEM[||||||||||||||||||18.966Gi/24.000Gi]

karolist• 2 years ago

He can use these cards for 128days non stop and re-sell, claiming back the purchase price almost fully since OP bought them cheap. Buying doesn't mean you use the GPUs to a point where they end up costing 0, yes there is risk with GPUs going but but c'mon.... Renting is money you will never see again.

lostmsu2• 2 years ago

I just made a clone of diskprices.com for GPUs specifically for AI training, and it has a power and depreciation calculator: https://gpuprices.us

You can expect a GPU to last 5 years. So for 128 days break even you are only looking at 6.67% utilization. If you are doing training runs, I think you are going to beat it easily.

P.S. coincidentally or not, but shortly after it got mentioned on Hacker News, Best Buy run out of both RTX 4090s and RTX 4080s. They used to top the chart. Turns out at descent utilization they win due to the electricity costs.

leobg3• 2 years ago

Exactly. And you rarely see machines from Germany on vast. Might as well run a data center in Bermuda. [0]

[0] https://www.royalgazette.com/general/business/article/202307...

cyanydeez4• 2 years ago

just ordered a 15k thread ripper platform because it's the only way to cheaply maximize the pcie16x bottleneck. the mining rigs are neat because the space you need for consumer GPU is a big issue.

those rigs need pcie riser slots that are also limited.

looks like the primary value is the rig and the cards. they'll need another 1-2k for a thread ripper and then the riser slots.

dijit• 2 years ago

availability is tight i think but check out the ampere altra stuff, they have an absurd number of pci’s lanes compared to AMD and especially intel, if you can suffer the ARM architecture.

They also have some ML inference stuff on chip themselves.

choppaface2• 2 years ago

But then you need to deal with arm compile issues. A lot of common packages are available for arm, but x86 is still least likely to distract your development.

segmondy7• 2 years ago

Unless you are training maximizing the PCIe lanes is truly overrated. You certainly don't want to be running at 1x speed. But 8x speed is enough with minimal impact. 8*3 = 32 lanes. Most CPUs can provide that. I'm running off a 2012 hp z820, that yields 3x16/1x8. So for anyone going for a build, don't throw money on CPUs. IMHO, GPU first, then your motherboard second (read the specs sheets), then CPU supported pcie lanes & Storage speed.

kaycebasques• 2 years ago

I really enjoy and am inspired by the idea that people like Dettmer (and probably this Samsja person) are the spiritual successors to homebrew hackers in the 70s and 80s. They have pretty intimate knowledge of many parts of the whole goddamn stack, from what's going on in each hardware component, to how to assemble all the components into a rig, up to all the software stuff: algorithms, data, orchestration, etc.

Am also inspired by embedded developers for the same reason

nirav72• 2 years ago

This is nice. I would’ve used one of those ETH mining cases that support multiple GPUs. Ebay has them $100-150 these days.

whoisthemachine• 2 years ago

I've been slowly expanding my HTPC/media server into a gaming server and box for running LLMs (and possibly diffusion models?) locally for playing around with. I think it's becoming clear that the future of LLM's will be local!

My box has a Gigabyte B450M, Ryzen 2700X, 32GB RAM, Radeon 6700XT (for gaming/streaming to steam link on Linux), and an "old" Geforce GTX 1650 with a paltry 6GB of RAM for running models on. Currently it works nicely with smaller models on ollama :) and it's been fun to get it set up. Obviously, now that the software is running I could easily swap in a more modern NVidia card with little hassle!

I've also been eyeing the b450 steel legend as a more capable board for expansion than the Gigabyte board, this article gives me some confidence that it is a solid board.

Uehreka• 2 years ago

I just got my hands on a mining rig with 3 rtx 3090 founder edition for the modest sum of 1.7k euros.

I would prefer a tutorial on how to do this.

gigatexal• 2 years ago

I thought this looked like a cryptocurrency miner. Seems the crypto to AI pivot is legit happening. And good. Would rather we boiled the oceans for something marginally more valuable than in-game tokens we traded for fiat funds in this video game we call life.

neilv• 2 years ago

For large VRAM models, what about selling one of the 3090s, and putting the money towards an NVLink and a motherboard with two x16 PCIe slots (and preferably spaced so you don't need riser cables)?

p1esk2• 2 years ago

Why do you need x16 pcie slots if you can use nvlink?

elorant• 2 years ago

NVlink is to connect the cards to each other. To connect them to the board you need the PCie slots.

segmondy7• 2 years ago

full riser cables like they used doesn't impact performance. Hanging it off on open air frame IMO is better, keeps everything cooler, not just the GPU but the motherboard and surrounding components. With only 2 24gb GPU they are not going to be able to run larger models. You can't experiment with 70b models without offloading to CPU which is super slow. The best models are 70b+ models.

ImprobableTruth2• 2 years ago

48 gb suffice for 4-bit inference and q-lora training of a 70b model. ~80 GB allows you to push it to 8-bit (which is nice of course), but full precision finetuning is completely out of reach either way.

Though you're right of course that pcie will totally suffice for this case.

ImprobableTruth2• 2 years ago

IME NVLink would be overkill for this. Model parallelism means you only need bandwidth to transfer the intermediate activations (/gradients + optimizer state) at the seams and inference speed is generally slow enough that even pcie x8 won't be a bottleneck.

whimsicalism8• 2 years ago

I strongly, strongly suspect most people doing this are significantly short of the breakeven prices for transitioning from cloud 3090s.

inb4 there are no cloud 3090s: yes there are, just not in formal datacenters

soraki_soladead• 2 years ago

It's not always about cost. Sometimes the ergonomics of a local machine are nicer.

smokeydoe• 2 years ago

Does anyone have any good recommendations for an epyc server grade motherboard that can use 3x3090? My current motherboard (strix trx40-xe) has memory issues now. 2 slots cause boot errors no matter what memory is inserted. I plan to sell the threadripper. Other option is to just swap out the current motherboard with a trx zenith extreme but I feel server grade would be better at this point after experiencing issues. Is supermicro worth it?

KuriousCat3• 2 years ago

It might not be the answer you are looking for, I would take a look at components published by System76/Lambda labs such as this to pick the one that would suit me: https://github.com/system76/thelio/blob/master/Thelio%20Comm...

segmondy7• 2 years ago

If you're just going to stick to 3 GPUs. Then a lot of consumer gaming motherboards would be more than sufficient. Checkout the z270, x99, x299. If you really want epyc go to ebay search for "gigabyte mz32-ar0 motherboard" Majority of them are going to come form China and they are all pretty much used. If you have plans to go even bigger then I say go for a new wrx80

buildbot2• 2 years ago

I have this motherboard - a big downside is many of the PCIE slots will overhang into the RAM if used for a GPU. I can't use two channels in my current ML machine because of this, and I have single slot 4090s.

devbug• 2 years ago

H12SSL-i or H12SSL-NT

ROMED8U-2T

moondev• 2 years ago

Heh I went the opposite direction from zenith to ASRock tnt

Are you on the latest bios? I have had good results flashing the "E" bios in the TNT folder based on the FTP site mentioned here

https://www.reddit.com/r/ASRock/s/GztBuD9INh

Yenrabbit• 2 years ago

Note that they shared part two recently: https://samsja.github.io/blogs/rig/part_2/

For those talking about breakeven points and cheap cloud compute, you need to factor in the mental difference it makes running a test locally (which feels free) vs setting up a server and knowing you're paying per hour it's running. Even if the cost is low, I do different kinds of experiments knowing I'm not 'wasting money' every minute the GPU sits idle. Once something is working, then sure scaling up on cheap cloud compute makes sense. But it's really, really nice having local compute to get to that state.

buildbot2• 2 years ago

Lots of people really underestimate the impact of that mental state and the activation energy it creates towards doing experiments - having some local compute is essential!

krallistic• 2 years ago

This. In the second article, the author touches on this a bit.

With a local setup, I often think, "Might as well run that weird xyz experiment over night" (instead of idling) On a cloud setup, the opposite is often the case: "Do I really need that experiment or can I shut down the sever to save money?". Makes a huge difference over longer periods.

For companies or if you just want to try a bit, then the cloud is a good option, but for (Ph.D.) researchers, etc., the frictionless local system is quite powerful.

ummonk• 2 years ago

I have the same attitude towards gym memberships - it really helps to know I can just go in for 30 minutes when I feel like it without worrying whether I’d be getting my money’s worth.

abra03• 2 years ago

That's a great point! I'd agree that just the extra emotional motivation from having your own thing is worth a ton. I get some distance down that way by having a large RAM no GPU box, so that things are slow but at least possible for random small one offs.

leobg3• 2 years ago

It depends on your local energy prices and your internet speed as well. It may actually be cheaper and faster to spin up a cloud instance. I bought an 80 core server to do some heavy lifting back in 2020. It doesn't even have a GPU, but it costs me around 4 euros per day to run. For that price I can keep a cloud GPU instance running. And even the boot up time isn’t any slower.

KuriousCat3• 2 years ago

What cloud GPU instance are you talking about here? Most of the GPUs cost around 2 to 40 dollars an hour. I would love to know the provider who is offering one for 4 dollars a day...

0x20cowboy• 2 years ago

If you would like to put Kubernetes on top of this kind of setup this repo is helpful https://github.com/robrohan/skoupidia

The main benefit is you can shut off nodes entirely when not using them, and then when you turn them back on they just rejoin the cluster.

It also helps managing different types of devices and workloads (tpu vs gpu vs cpu)

2OEH8eoCRo0• 2 years ago

I love the idea of a "poor man's cluster" of hardware that I can continually add to. Old ereaders, phones, tablets, family laptops, everything.

I'm not sure what I'd use it for.

bick_nyers• 2 years ago

Somewhat tangential question, but I'm wondering if anyone knows of a solution (or Google search terms for this):

I have a 3U supermicro server chassis that I put an AM4 motherboard into, but I'm looking at upgrading the Mobo so that I can run ~6 3090s in it. I don't have enough physical PCIE slots/brackets in the chassis (7 expansion slots), so I either need to try to do some complicated liquid cooling setup to make the cards single slot (I don't want to do this), or I need to get a bunch of riser cables and mount the GPU above the chassis. Is there like a JBOD equivalent enclosure for PCIE cards? I don't really think I can run the risers out the back of the case, so I'll likely need to take off/modify the top panel somehow. What I'm picturing in my head is basically a 3U to 6U case conversion, but I'm trying to minimize cost (let's say $200 for the chassis/mount component) as well as not have to cut metal.

choppaface2• 2 years ago

Comino sells a 6x 4090 box as a product: https://www.comino.com/

They have single-slot GPU waterblocks but would want something like $400 or more each for them individually.

ftufek• 2 years ago

You'll need something like EPYC/Xeon CPUs and motherboards which not only have many more PCIe lanes, but also allow bifurcation. Once you have that, you can get bifurcated risers and have many GPUs. And these risers use normal cables not the typical gamer pcie risers which are pretty hard to arrange. You won't get this for just $200 though.

For the chassis, you could try a 4U rosewill like this: https://www.youtube.com/watch?v=ypn0jRHTsrQ, not sure if 6 3090s would fit though. You're probably better off getting a mining chassis, it's easier to setup and cool down, also cheaper, unless you plan on putting them in a server rack.

jeffybefffy5192• 2 years ago

Are m1/m2/m3 max mac's any good for this?

downrightmike2• 2 years ago

Way slower than 1 gpu, at many times the cost. If you don't mind waiting minutes instead of seconds, macs are reasonable

fragmede2• 2 years ago

It depends on what you're trying to do, but I've got an M1, and doing inference with llama2-uncensored using Ollama, I get results within seconds.

akasakahakada• 2 years ago

Just sharing.

2 x RTX4090 workstation guide

You can put two aircooled 4090 in the same ATX case if you do enough research.

https://github.com/eul94458/Memo/blob/main/dual_rtx4090works...

Building a deep learning rig

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