The fact that is is from LA Times shows that it’s still significant though
Wether we like it or not AI is here to stay, and in 20-30 years, it’ll be as embedded in our lives as computers and smartphones are now.
Right, it did have an AI winter few decades ago. It’s indeed here to stay, it doesn’t many any of the current company marketing it right now will though.
AI as a research field will stay, everything else maybe not.
Is there a “young man yells at clouds meme” here?
“Yes, you’re very clever calling out the hype train. Oooh, what a smart boy you are!” Until the dust settles…
Lemmy sounds like my grandma in 1998, “Pushah. This ‘internet’ is just a fad.'”
Removed by mod
Yeah, the early Internet didn’t require 5 tons of coal be burned just to give you a made up answer to your query. This bubble is Pets.com only it is also murdering the rainforest while still be completely useless.
Estimates for chatgpt usage per query are on the order of 20-50 Wh, which is about the same as playing a demanding game on a gaming pc for a few minutes. Local models are significantly less.
Can you imagine all the troll farms automatically using all this power
Removed by mod
FMO is the best explanation of this psychosis and then of course denial by people who became heavily invested in it. Stuff like LLMs or ConvNets (and the likes) can already be used to do some pretty amazing stuff that we could not do a decade ago. I am also not against exploring and pushing the boundaries, but when you explore a boundary while pretending like you have already crossed it, that is how you get bubbles. And this again all boils down to appeasing some cancerous billionaire shareholders so they funnel down some money to your pockets.
Stuff like LLMs or ConvNets (and the likes) can already be used to do some pretty amazing stuff that we could not do a decade ago, there is really no need to shit rainbows and puke glitter all over it.
I’m shitting rainbows and puking glitter on a daily basis BUT it’s not against AI as a field, it’s not against AI research, rather it’s against :
- catastrophism and fear, even eschatology, used as a marketing tactic
- open systems and research that become close
- trying to lock a market with legislation
- people who use a model, especially a model they don’t even have e.g using a proprietary API, and claim they are an AI startup
- C-levels decision that anything now must include AI
- claims that this or that skill is soon to be replaced by AI with actually no proof of it
- meaningless test results with grand claim like “passing the bar exam” used as marketing tactics
- claims that it scales, it “just needs more data”, not for .1% improvement but for radical change, e.g emergent learning
- for-profit (different from public research) scrapping datasets without paying back anything to actual creators
- ignoring or lying about non renewable resource consumption for both training and inference
- relying on “free” or loss leader strategies to dominate a market
- promoting to be doing the work for the good of humanity then signing exclusive partnership with a corporation already fined for monopoly practices
I’m sure I’m forgetting a few but basically none of those criticism are technical. None of those criticism is about the current progress made. Rather, they are about business practices.
there is really no need shit rainbows and puke glitter all over it
I’m now picturing the unicorn from the Squatty Potty commercial, with violent diarrhea and vomiting.
A.I., Assumed Intelligence
More like PISS, a Plagiarized Information Synthesis System
Welp, it was ‘fun’ while it lasted. Time for everyone to adjust their expectations to much more humble levels than was promised and move on to the next sceme. After Metaverse, NFTs and ‘Don’t become a programmer, AI will still your job literally next week!11’, I’m eager to see what they come up with next. And with eager I mean I’m tired. I’m really tired and hope the economy just takes a damn break from breaking things.
I just hope I can buy a graphics card without having to sell organs some time in the next two years.
My RX 580 has been working just fine since I bought it used. I’ve not been able to justify buying a new (used) one. If you have one that works, why not just stick with it until the market gets flooded with used ones?
I’d love an upgrade for my 2080 TI, really wish Nvidia didn’t piss off EVGA into leaving the GPU business…
Don’t count on it. It turns out that the sort of stuff that graphics cards do is good for lots of things, it was crypto, then AI and I’m sure whatever the next fad is will require a GPU to run huge calculations.
AI is shit but imo we have been making amazing progress in computing power, just that we can’t really innovate atm, just more race to the bottom.
——
I thought capitalism bred innovation, did tech bros lied?
/s
I’m sure whatever the next fad is will require a GPU to run huge calculations.
I also bet it will, cf my earlier comment on rendering farm and looking for what “recycles” old GPUs https://lemmy.world/comment/12221218 namely that it makes sense to prepare for it now and look for what comes next BASED on the current most popular architecture. It might not be the most efficient but probably will be the most economical.
If there is even a GPU being sold. It’s much more profitable for Nvidia to just make compute focused chips than upgrading their gaming lineup. GeForce will just get the compute chips rejects and laptop GPU for the lower end parts. After the AI bubble burst, maybe they’ll get back to their gaming roots.
move on to the next […] eager to see what they come up with next.
That’s a point I’m making in a lot of conversations lately : IMHO the bubble didn’t pop BECAUSE capital doesn’t know where to go next. Despite reports from big banks that there is a LOT of investment for not a lot of actual returns, people are still waiting on where to put that money next. Until there is such a place, they believe it’s still more beneficial to keep the bet on-going.
But if it doesn’t disrupt it isn’t worth it!
/s
Wall Street has already milked “the pump” now they short it and put out articles like this
Too much optimism and hype may lead to the premature use of technologies that are not ready for prime time.
— Daron Acemoglu, MIT
Preach!
It’s like the least popular opinion I have here on Lemmy, but I assure you, this is the begining.
Yes, we’ll see a dotcom style bust. But it’s not like the world today wasn’t literally invented in that time. Do you remember where image generation was 3 years ago? It was a complete joke compared to a year ago, and today, fuck no one here would know.
When code generation goes through that same cycle, you can put out an idea in plain language, and get back code that just “does” it.
I have no idea what that means for the future of my humanity.
you can put out an idea in plain language, and get back code that just “does” it
No you can’t. Simplifying it grossly:
They can’t do the most low-level, dumbest detail, splitting hairs, “there’s no spoon”, “this is just correct no matter how much you blabber in the opposite direction, this is just wrong no matter how much you blabber to support it” kind of solutions.
And that happens to be main requirement that makes a task worth software developer’s time.
We need software developers to write computer programs, because “a general idea” even in a formalized language is not sufficient, you need to address details of actual reality. That is the bottleneck.
That technology widens the passage in the places which were not the bottleneck in the first place.
they’re pretty good, and the faults they have are improving steadily. I dont think we’re hitting a ceiling yet, and I shudder to think where they’ll be in 5 years.
I think you live in a nonsense world. I literally use it everyday and yes, sometimes it’s shit and it’s bad at anything that even requires a modicum of creativity. But 90% of shit doesn’t require a modicum of creativity. And my point isn’t about where we’re at, it’s about how far the same tech progressed on another domain adjacent task in three years.
Lemmy has a “dismiss AI” fetish and does so at its own peril.
And my point isn’t about where we’re at, it’s about how far the same tech progressed on another domain adjacent task in three years.
First off, are you extrapolating the middle part of the sigmoid thinking it’s an exponential. Secondly, https://link.springer.com/content/pdf/10.1007/s11633-017-1093-8.pdf
I’ve written something vague in another place in this thread which seemed a good enough argument. But I didn’t expect that someone is going to link a literal scientific publication in the same very direction. Thank you, sometimes arguing in the Web is not a waste of time.
EDIT: Have finished reading it. Started thinking it was the same argument, in the middle got confused, in the end realized that yes, it’s the same argument, but explained well by a smarter person. A very cool article, and fully understandable for a random Lemming at that.
Dismiss at your own peril is my mantra on this. I work primarily in machine vision and the things that people were writing on as impossible or “unique to humans” in the 90s and 2000s ended up falling rapidly, and that generation of opinion pieces are now safely stored in the round bin.
The same was true of agents for games like go and chess and dota. And now the same has been demonstrated to be coming true for languages.
And maybe that paper built in the right caveats about “human intelligence”. But that isn’t to say human intelligence can’t be surpassed by something distinctly inhuman.
The real issue is that previously there wasn’t a use case with enough viability to warrant the explosion of interest we’ve seen like with transformers.
But transformers are like, legit wild. It’s bigger than UNETs. It’s way bigger than ltsm.
So dismiss at your own peril.
But that isn’t to say human intelligence can’t be surpassed by something distinctly inhuman.
Tell me you haven’t read the paper without telling me you haven’t read the paper. The paper is about T2 vs. T3 systems, humans are just an example.
Yeah I skimmed a bit. I’m on like 4 hours of in flight sleep after like 24 hours of air ports and flying. If you really want me to address the points of the paper, I can, but I can also tell it doesn’t diminish my primary point: dismiss at your own peril.
dismiss at your own peril.
Oooo I’m scared. Just as much as I was scared of missing out on crypto or the last 10000 hype trains VCs rode into bankruptcy. I’m both too old and too much of an engineer for that BS especially when the answer to a technical argument, a fucking information-theoretical one on top of that, is “Dude, but consider FOMO”.
That said, I still wish you all the best in your scientific career in applied statistics. Stuff can be interesting and useful aside from AI BS. If OTOH you’re in that career path because AI BS and not a love for the maths… let’s just say that vacation doesn’t help against burnout. Switch tracks, instead, don’t do what you want but what you can.
Or do dive into AGI. But then actually read the paper, and understand why current approaches are nowhere near sufficient. We’re not talking about changes in architecture, we’re about architectures that change as a function of training and inference, that learn how to learn. Say goodbye to the VC cesspit, get tenure aka a day job, maybe in 50 years there’s going to be another sigmoid and you’ll have written one of the papers leading up to it because you actually addressed the fucking core problem.
And I wouldn’t know where to start using it. My problems are often of the “integrate two badly documented company-internal APIs” variety. LLMs can’t do shit about that; they weren’t trained for it.
They’re nice for basic rote work but that’s often not what you deal with in a mature codebase.
Again, dismiss at your own peril.
Because “Integrate two badly documented APIs” is precisely the kind of tasks that even the current batch of LLMs actually crush.
And I’m not worried about being replaced by the current crop. I’m worried about future frameworks on technology like greyskull running 30, or 300, or 3000 uniquely trained LLMs and other transformers at once.
I’m with you. I’m a Senior software engineer and copilot/chatgpt have all but completely replaced me googling stuff, and replaced 90% of the time I’ve spent writing the code for simple tasks I want to automate. I’m regularly shocked at how often copilot will accurately auto complete whole methods for me. I’ve even had it generate a whole child class near perfectly, although this is likely primarily due to being very consistent with my naming.
At the very least it’s an extremely valuable tool that every programmer should get comfortable with. And the tech is just in it’s baby form. I’m glad I’m learning how to use it now instead of pooh-poohing it.
Ikr? It really seems like the dismissiveness is coming from people either not experienced with it, or just politically angry at its existence.
Are you a software developer? Or a hardware engineer? EDIT: Or anyone credible in evaluating my nonsense world against yours?
Machine learning scientist.
So close, but not there.
OK, you’ll know that I’m right when you somewhat expand your expertise to neighboring areas. Should happen naturally.
That explains your optimism. Code generation is at a stage where it slaps together Stack Overflow answers and code ripped off from GitHub for you. While that is quite effective to get at least a crappy programmer to cobble together something that barely works, it is a far cry from having just anyone put out an idea in plain language and getting back code that just does it. A programmer is still needed in the loop.
I’m sure I don’t have to explain to you that AI development over the decades has often reached plateaus where the approach needed to be significantly changed in order for progress to be made, but it could certainly be the case where LLMs (at least as they are developed now) aren’t enough to accomplish what you describe.
It’s not about stages. It’s about the Achilles and tortoise problem.
There’s extrapolation inside the same level of abstraction as the data given and there’s extrapolation of new levels of abstraction.
But frankly far smarter people than me are working on all that. Maybe they’ll deliver.
this is just wrong no matter how much you blabber to support it" kind of solutions.
When you put it like that, I might be a perfect fit in today’s world with the loudest voice wins landscape.
I regularly think and post conspiracy theory thoughts about why the “AI” is such a hype. And in line with them a certain kind of people seem to think that reality doesn’t matter, because those who control the present control the past and the future. That is, they think that controlling the discourse can replace controlling the reality. The issue with that is that whether a bomb is set, whether a boat is sea-worthy, whether a bridge will fall is not defined by discourse.
I agree with you but not for the reason you think.
I think the golden age of ML is right around the corner, but it won’t be AGI.
It would be image recognition and video upscaling, you know, the boring stuff that is not game changing but possibly useful.
I feel the same about the code generation stuff. What I really want is a tool that suggests better variable names.
I’m just praying people will fucking quit it with the worries that we’re about to get SKYNET or HAL when binary computing would inherently be incapable of recreating the fast pattern recognition required to replicate or outpace human intelligence.
Moore’s law is about similar computing power, which is a measure of hardware performance, not of the software you can run on it.
Unfortunately it’s part of the marketing, thanks OpenAI for that “Oh no… we can’t share GPT2, too dangerous” then… here it is. Definitely interesting then but now World shattering. Same for GPT3 … but through exclusive partnership with Microsoft, all closed, rinse and repeat for GPT4. It’s a scare tactic to lock what was initially open, both directly and closing the door behind them through regulation, at least trying to.
My only real hope out of this is that that copilot button on keyboards becomes the 486 turbo button of our time.
Meaning you unpress it, and computer gets 2x faster?
Actually you pressed it and everything got 2x slower. Turbo was a stupid label for it.
That’s… the same thing.
Whops, I thought you were responding to the first child comment.
I could be misremembering but I seem to recall the digits on the front of my 486 case changing from 25 to 33 when I pressed the button. That was the only difference I noticed though. Was the beige bastard lying to me?
Lying through its teeth.
There was a bunch of DOS software that runs too fast to be usable on later processors. Like a Rouge-like game where you fly across the map too fast to control. The Turbo button would bring it down to 8086 speeds so that stuff is usable.
Damn. Lol I kept that turbo button down all the time, thinking turbo = faster. TBF to myself it’s a reasonable mistake! Mind you, I think a lot of what slowed that machine was the hard drive. Faster than loading stuff from a cassette tape but only barely. You could switch the computer on and go make a sandwich while windows 3.1 loads.
Oh, yeah, a lot of people made that mistake. It was badly named.
I was thinking pressing it turns everything to shit, but that works too. I’d also accept, completely misunderstood by future generations.
Well now I wanna hear more about the history of this mystical shit button
Back in those early days many applications didn’t have proper timing, they basically just ran as fast as they could. That was fine on an 8mhz cpu as you probably just wanted stuff to run as fast as I could (we weren’t listening to music or watching videos back then). When CPUs got faster (or it could be that it started running at a multiple of the base clock speed) then stuff was suddenly happening TOO fast. The turbo button was a way to slow down the clock speed by some amount to make legacy applications run how it was supposed to run.
Most turbo buttons never worked for that purpose, though, they were still way too fast Like, even ignoring other advances such as better IPC (or rather CPI back in those days) you don’t get to an 8MHz 8086 by halving the clock speed of a 50MHz 486. You get to 25MHz. And practically all games past that 8086 stuff was written with proper timing code because devs knew perfectly well that they’re writing for more than one CPU. Also there’s software to do the same job but more precisely and flexibly.
It probably worked fine for the original PC-AT or something when running PC-XT programs (how would I know our first family box was a 386) but after that it was pointless. Then it hung on for years, then it vanished.
The more you bAI
But the trillion dollar valued Nvidia…
I think they’re going to be bankrupt within 5 years. They have way too much invested in this bubble.
NVIDIA uses of AI technology aren’t going to pop, things like DLSS are here to stay. The value of the company and their sales are inflated by the bubble, but the core technology of NVIDIA is applicable way beyond the chat bot hype.
Bubbles don’t mean there’s no underlying value. The dot com bubble didn’t take down the internet.
Fall in share price, yes.
Bankrupt, no. Their debt to Equity Ratio is 0.1455. They can pay off their $11.23 B debt with 2 months of revenue. They can certainly afford the interest payments.
I highly doubt that. If the AI bubble pops, they’ll probably be worth a lot less relative to other tech companies, but hardly bankrupt. They still have a very strong GPU business, they probably have an agreement with Nintendo on the next Switch (like they did with the OG Switch), and they could probably repurpose the AI tech in a lot of different ways, not to mention various other projects where they package GPUs into SOCs.
It really depends on how much they’ve invested in building AI chips.
Sure, but their deliveries have also been incredibly large. I’d be surprised if they haven’t already made enough from previous sales to cover all existing and near-term investments into AI. The scale of the build-out by big cloud firms like Amazon, Google, and Microsoft has been absolutely incredible, and Nvidia’s only constraint has been making enough of them to sell. So even if support completely evaporates, I think they’ll be completely fine.
They don’t build the chips at all. They pay tsmc.
I’m not sure, these companies are building data centers with so many gpus that they have to be geo located with respect to the power grid because if it were all done in one place it would take the grid down.
And they are just building more.
But the company doesn’t have the money. Stock value means investor valuation, not company funds.
Once a company goes public for the very first time, it’s getting money into its account, but from then on forward, that’s just investors speculating and hoping on a nice return when they sell again.
Of course there should be some correlation between the company’s profitability and the stock price, so ideally they do have quite some money, but in an investment craze like this, the correlation is far from 1:1. So whether they can still afford to build the data centers remains to be seen.
They’re not building them for themselves, they’re selling GPU time and SuperPods. Their valuation is because there’s STILL a lineup a mile long for their flagship GPUs. I get that people think AI is a fad, and it’s public form may be, but there’s thousands of GPU powered projects going on behind closed doors that are going to consume whatever GPUs get made for a long time.
Their valuation is because there’s STILL a lineup a mile long for their flagship GPUs.
Genuinely curious, how do you know where the valuation, any valuation, come from?
This is an interesting story, and it might be factually true, but as far as I know unless someone has actually asked the biggest investor WHY they did bet on a stock, nobody why a valuation is what it is. We might have guesses, and they might even be correct, but they also change.
I mentioned it few times here before but my bet is yes, what you did mention BUT also because the same investors do not know where else do put their money yet and thus simply can’t jump boats. They are stuck there and it might again be become they initially though the demand was high with nobody else could fulfill it, but I believe that’s not correct anymore.
Well, I’m no stockologist, but I believe when your company has a perpetual sales backlog with a 15-year head start on your competition, that should lead to a pretty high valuation.
but I believe that’s not correct anymore.
Why do you believe that? As far as I understand, other HW exists…but no SW to run on it…
Right, and I mentioned CUDA earlier as one of the reason of their success, so it’s definitely something important. Clients might be interested in e.g Google TPU, startups like Etched, Tenstorrent, Groq, Cerebras Systems or heck even design their own but are probably limited by their current stack relying on CUDA. I imagine though that if backlog do keep on existing there will be abstraction libraries, at least for the most popular ones e.g TensorFlow, JAX or PyTorch, simply because the cost of waiting is too high.
Anyway what I meant isn’t about hardware or software but rather ROI, namely when Goldman Sachs and others issue analyst report saying that the promise itself isn’t up to par with actual usage for paying customers.
Yeah, someone else commented with their financials and they look really good, so while I certainly agree that they are overvalued because we are in an AI training bubble, I don’t see it popping for a few years, especially given that they are selling the shovels. every big player in the space is set on orders of magnitude of additional compute for the next 2 years or more. It doesn’t matter if the company they sold gpus to fails if they already sold them. Something big that unexpected would have to happen to upset that trajectory right now and I don’t see it because companies are in the exploratory stage of ai tech so no one knows what doesn’t work until they get the computer they need. I could be wrong, but that’s what I see as a watcher of ai news channels on YouTube.
The co founder of open AI just got a billion dollars for his new 3 month old AI start up. They are going to spend that money on talent and compute. X just announced a data center with 100,000 gpus for grok2 and plans to build the largest in the world I think? But that’s Elon, so grains of salt and all that are required there. Nvidia are working with robotics companies to make AI that can train robots virtually to do a task and in the real world a robot will succeed first try. No more Boston dynamics abuse compilation videos. Right now agentic ai workflow is supposed to be the next step, so there will be overseer ai algorithms to develop and train.
All that is to say there is a ton of work that requires compute for the next few years.
{Opinion here} – I feel like a lot of people are seeing grifters and a wobbly gpt4o launch and calling the game too soon. It takes time to deliver the next product when it’s a new invention in its infancy and the training parameters are scaling nearly logarithmically from gen to gen.
I’m sure the structuring of payment for the compute devices isn’t as simple as my purchase of a gaming GPU from microcenter, but Nvidia are still financially sound. I could see a lot of companies suffering from this long term but nvidia will be The player in AI compute, whatever that looks like, so they are going to bounce back and be fine.
$2.5T currently to be exact
Nvidia is diversified in AI, though. Disregarding LLM, it’s likely that other AI methodologies will depend even more on their tech or similar.
Maybe we can have normal priced graphics cards again.
I’m tired of people pretending £600 is a reasonable price to pay for a mid range GPU.
deleted by creator
Its all vibes and manipulation
The broader market did the same thing
https://finance.yahoo.com/quote/SPY/
$560 to $510 to $560 to $540
So why did $NVDA have larger swings? It has to do with the concept called beta. High beta stocks go up faster when the market is up and go down lower when the market is done. Basically high variance risky investments.
Why did the market have these swings? Because of uncertainty about future interest rates. Interest rates not only matter vis-a-vis business loans but affect the interest-free rate for investors.
When investors invest into the stock market, they want to get back the risk free rate (how much they get from treasuries) + the risk premium (how much stocks outperform bonds long term)
If the risks of the stock market are the same, but the payoff of the treasuries changes, then you need a high return from stocks. To get a higher return you can only accept a lower price,
This is why stocks are down, NVDA is still making plenty of money in AI
There’s more to it as well, such as:
- investors coming back from vacation and selling off losses and whatnot
- investors expecting reduced spending between summer and holidays; we’re past the “back to school” retail bump and into a slower retail economy
- upcoming election, with polls shifting between Trump and Harris
September is pretty consistently more volatile than other months, and has net negative returns long-term. So it’s not just the Fed discussing rate cuts (that news was reported over the last couple months, so it should be factored in), but just normal sideways trading in September.
We already knew about back to school sales, they happen every year and they are priced in. If there was a real stock market dump every year in September, everyone would short September, making a drop in August and covering in September, making September a positive month again
It’s not every year, but it is more than half the time. Source:
History suggests September is the worst month of the year in terms of stock-market performance. The S&P 500 SPX has generated an average monthly decline of 1.2% and finished higher only 44.3% of the time dating back to 1928, according to Dow Jones Market Data.
S&P 500 up this September officially
Woo! We’re part of the 44% or so. :)
45% now since the data only goes back 100 years
Also bubbles don’t “leak”.
I mean, sometimes they kinda do? They either pop or slowly deflate, I’d say slow deflation could be argued to be caused by a leak.
deleted by creator
You can do it easily with a balloon (add some tape then poke a hole). An economic bubble can work that way as well, basically demand slowly evaporates and the relevant companies steadily drop in value as they pivot to something else. I expect the housing bubble to work this way because new construction will eventually catch up, but building new buildings takes time.
The question is, how much money (tape) are the big tech companies willing to throw at it? There’s a lot of ways AI could be modified into niche markets even if mass adoption doesn’t materialize.
deleted by creator
You do realize an economic bubble is a metaphor, right? My point is that a bubble can either deflate rapidly (severe market correction, or a “burst”), or it can deflate slowly (a bear market in a certain sector). I’m guessing the industry will do what it can to have AI be the latter instead of the former.
deleted by creator
One good example of a bubble that usually deflates slowly is the housing market. The housing market goes through cycles, and those bubbles very rarely pop. It popped in 2008 because banks were simultaneously caught with their hands in the candy jar by lying about risk levels of loans, so when foreclosures started, it caused a domino effect. In most cases, the fed just raises rates and housing prices naturally fall as demand falls, but in 2008, part of the problem was that banks kept selling bad loans despite high mortgage rates and high housing prices, all because they knew they could sell those loans off to another bank and make some quick profit (like a game of hot potato).
In the case of AI, I don’t think it’ll be the fed raising rates to cool the market (that market isn’t impacted as much by rates), but the industry investing more to try to revive it. So Nvidia is unlikely to totally crash because it’ll be propped up by Microsoft, Amazon, and Google, and Microsoft, Apple, and Google will keep pitching different use cases to slow the losses as businesses pull away from AI. That’s quite similar to how the fed cuts rates to spur economic investment (i.e. borrowing) to soften the impact of a bubble bursting, just driven from mega tech companies instead of a government.
At least that’s my take.
We taking about bubbles or are we talking about balloons? Maybe we should change to using the word balloon instead, since these economic ‘bubbles’ can also deflate slowly.
Good point, not sure that economists are human enough to take sense into account, but I think we should try and make it a thing.
Personally I can’t wait for a few good bankruptcies so I can pick up a couple of high end data centre GPUs for cents on the dollar
Search Nvidia p40 24gb on eBay, 200$ each and surprisingly good for selfhosted llm, if you plan to build array of gpus then search for p100 16gb, same price but unlike p40, p100 supports nvlink, and these 16gb is hbm2 memory with 4096bit bandwidth so it’s still competitive in llm field while p40 24gb is gddr5 so it’s good point is amount of memory for money it cost but it’s rather slow compared to p100 and compared to p100 it doesn’t support nvlink
Can it run crysis?
How about cyberpunk?
I ran doom on a GPU!
Thanks for the tips! I’m looking for something multi-purpose for LLM/stable diffusion messing about + transcoder for jellyfin - I’m guessing that there isn’t really a sweet spot for those 3. I don’t really have room or power budget for 2 cards, so I guess a P40 is probably the best bet?
Try ryzen 8700g integrated gpu for transcoding since it supports av1 and these p series gpus for llm/stable diffusion, would be a good mix i think, or if you don’t have budget for new build, then buy intel a380 gpu for transcoding, you can attach it as mining gpu through pcie riser, linus tech tips tested this gpu for transcoding as i remember
Intel a310 is the best $/perf transcoding card, but if P40 supports nvenc, it might work for both transcode and stable diffusion.
Digging into it a bit more, it seems like I might be better off getting a 12gb 3060 - similar price point, but much newer silicon
Lowest price on Ebay for me is 290 Euro :/ The p100 are 200 each though.
Do you happen to know if I could mix a 3700 with a p100?
And thanks for the tips!
Ryzen 3700? Or rtx 3070? Please elaborate
Oh sorry, nvidia RTX :) Thanks!
I looked it up, rtx 3070 have nvlink capabilities though i wonder if all of them have it, so you can pair it if it have nvlink capabilities
Personally I don’t much for the LLM stuff, I’m more curious how they perform in Blender.
Interesting, I did try a bit of remote rendering on Blender (just to learn how to use via CLI) so that makes me wonder who is indeed scrapping the bottom of the barrel of “old” hardware and what they are using for. Maybe somebody is renting old GPUs for render farms, maybe other tasks, any pointer of such a trend?
