It is when those anticheats gatekeep the most popular PC games. For most gamers, they can't compromise on what they play and there is still a very large amount of potential games that would forbid a switch to another OS. See : https://areweanticheatyet.com/
When I decided to get back into PC gaming during covid, I built a PC put Windows on it and installed GOG, Steam and Epic to turn it into a glorified console. It has been like that ever since. For anything other than gaming I use a Macbook.
If you got the means and space, I think it's the easiest solution. I do play some games on the Mac, but the experience has been rather poor outside of indie games which usually work very well.
That said, the controller support on windows constantly sucks. On macOS though, it's really easy to set up. Go figure.
> I built a PC put Windows on it and installed GOG, Steam and Epic to turn it into a glorified console.
It's fine, if you are willing to put up with the forced logins, spyware, ads, unwanted cloud/AI integrations, requests to update/reboot when you don't want to, and dozens of other anti-features that suck up resources and actively work against the user.
Little known fact, the original DS touch screen is pressure sensitive. I don't know of any games using this feature, but the Colors homebrew does use it! So the DS was a fairly convenient digital art machine for its time.
I wonder why everyone keep saying "just put more VRAM" yet no cards seem to do that. If it is that easy to compete with Nvidia, why don't we already have those cards?
Maybe because only AI enthusiasts want that much VRAM, and most of them will pony up for a higher-end GPU anyways? Everyone is suggesting it here because that's what they want, but I don't know if this crowd is really representative of broader market sentiment.
There are a lot of local AI hobbyists, just visit /r/LocalLLama to see how many are using 8GB cards, or all the people asking for higher RAM version of cards.
This makes it mysterious since clearly CUDA is an advantage, but higher VRAM lower cost cards with decent open library support would be compelling.
There is no point in using a low-bandwidth card like the B50 for AI. Attempting to use 2x or 4x cards to load a real model will result in poor performance and low generation speed. If you don’t need a larger model, use a 3060 or 2x 3060, and you’ll get significantly better performance than the B50—so much better that the higher power consumption won’t matter (70W vs. 170W for a single card). Higher VRAM wont make the card 'better for AI'.
Are there any performance bottlenecks with using 2 cards instead of a single card? I don't think any one the consumer Nvidia cards use NVlink anymore, or at least they haven't for a while now.
If VRAM is ~$10/gb I suspect people paying $450 for a 12GB card would be happy to pay $1200 for a 64gb card. Running local LLM only uses about 3-6% of my GPU's capability, but all of it's VRAM. Local LLM has no need for 6 3090s to serve a single or handful of users; they just need the VRAM to run the model locally.
Exactly. People would be thrilled with a $1200 64GB card with ok processing power and transfer speed. It's a bit of a mystery why it doesn't exist. Intel is enabling vendors to 'glue' two 24GB cards together for a $1200 list price 48GB card, but it's a frankenstein monster and will probably not be available for that price.
Nvidia has zero incentives to undercut their enterprise GPUs by adding more RAM to “cheap” consumer cards like the 5090.
Intel and even AMD can’t compete or aren’t bothering. I guess we’ll see how the glued 48GB B60 will do, but that’s a still relatively slow GPU regardless of memory. Might be quite competitive with Macs, though.
r/LocalLLaMA has 90,000 subscribers. r/PCMasterRace has 9,000,000. I'll bet there are a lot more casual PC gamers who don't talk about it online than there are casual local AI users, too.
because the cards already sell at very very good prices with 16GB and optimizations in generative AI is bringing down memory requirements. Optimizing profits means yyou sell with the least amount of VRAM possible not only to save the direct cost of the RAM but also to guard future profit and your other market segments. the cost of the ram itself is almost nothing compared to that. any intel competitor can more easily release products with more than 16GB and smoke them. Intel tries for a market segment that was only served by gaming cards twice as expensive up until now. this frees those up to be finally sold at MSRP.
If intel was serious about staging a comeback, they would release a 64GB card.
But intel is still lost in it's hubris, and still thinks it's a serious player and "one of the boys", so it doesn't seem like they want to break the line.
> If it is that easy to compete with Nvidia, why don't we already have those cards?
Businesswise? Because Intel management are morons. And because AMD, like Nvidia, don't want to cannibalize their high end.
Technically? "Double the RAM" is the most straightforward (that doesn't make it easy, necessarily ...) way to differentiate as it means that training sets you couldn't run yesterday because it wouldn't fit on the card can now be run today. It also takes a direct shot at how Nvidia is doing market segmentation with RAM sizes.
Note that "double the RAM" is necessary but not sufficient.
You need to get people to port all the software to your cards to make them useful. To do that, you need to have something compelling about the card. These Intel cards have nothing compelling about them.
Intel could also make these cards compelling by cutting the price in half or dropping two dozen of these cards on every single AI department in the US for free. Suddenly, every single grad student in AI will know everything about your cards.
The problem is that Intel institutionally sees zero value in software and is incapable of making the moves they need to compete in this market. Since software isn't worth anything to Intel, there is no way to justify any business action isn't just "sell (kinda shitty) chip".
I believe that VRAM has massively shot up in price, so this is where a large part of the costs are. Besides I wouldn't be very surprised if Nvidia has such strong market share they can effectively tell suppliers to not let others sell high capacity cards. Especially because VRAM suppliers might worry about ramping up production too much and then being left with an oversupply situation.
This could well be the reason why the rumored RDNA5 will use LPDDR5X/LPDDR5X instead of GDDR7 memory, at least for the low/mid range configurations (the top-spec and enthusiast configurations AT0 and AT2 configurations will still use GDDR7 it seems).
It is not really clear if it will be called as UDNA or RDNA5, I was just referring to the next-gen graphics architecture from AMD and referring as RDNA5 is just clearer that this is the next-gen architecture.
I don't really know what I'm talking about (whether about graphic cards or in AI inference), but if someone figures out how to cut the compute needed for AI inference significantly then I'd guess the demand for graphic cards would suddenly drop?
Given how young and volatile this domain still is, it doesn't seem unreasonable to be wary of it. Big players (google, openai and the likes) are probably pouring tons of money into trying to do exactly that
I would suspect that for self hosted LLMs, quality >>> performance, so the newer releases will always expand to fill capacity of available hardware even when efficiency is improved.
There does seem to be a grey market for it in China. You can buy cards where they swap the memory modules with higher capacity ones on Aliexpress and ebay.
Ryzen AI max+ 395 128GB can do 256GBps so lets put all these "ifs" to bed once for all. That is absolutely no brainer to drop more RAM as long as there is enough bits in address space of physical hardware. And there usually is, as same silicons are branded and packaged differently for commercial market and for consumer market. Check up how chinese are doubling 4090s RAM from 24 to 48GB.
LLM can create new things, since their whole purpose is to interpolate concepts in a latent space. Unfortunately they are mostly used to regurgitate verbatim what they learned, see the whole AI Ghibli craze. Blame people and their narrow imagination.
> I think, on the contrary, that if a lion could talk, that lion would have a mind so different from the general run of lion minds, that although we could understand him just fine, we would learn little about ordinary lions from him.
That makes any kind of insight into consciousness as a general term impossible though. That would mean we could not learn anything about human consciousness as such from studying specific persons.
It's a great pull, because it has an important implication that I think ties in directly to Nagels point. Another fascinating variation of the same idea is "beetle in the box", another great one from Wittgenstein. I don't think I agree with him, because I think it hinges on assuming lions have fundamental and irreducibly different experiences. But I think we have important similarities due to our shared evolutionary heritage, and even from the outside I'm willing to die on the hill of insisting that Lions certainly do have experiences familiar to us, like hunger, pain, the satisfaction of having an itch scratched, having a visual field, and having the ability to distinguish shades and color (though their experience of color is likely importantly different from ours, but overlaps enough for there to be such a thing as shared meaning).
I don't understand why Wittgenstein wasn't more forcefully challenged on this. There's something to the principle as a linguistic principle, but it just feels overextended into a foundational assumption that their experiences are fundamentally unlike ours.
It is rather common in gaming to communities to find people completely obessed over ultra specific details of their favorite game. It isn't even the first time for Minecraft, see the "pack.png" case.
If you watched Westworld, this is what "the archives library of the Forge" represented. It was a vast digital archive containing the consciousness of every human guest who visited the park. And it was obtained through the hats they chose and wore during their visits and encounters.
Instead of hats, we have Anthropic, OpenAI and other services training on interactions with users who use "free" accounts. Think about THAT for a moment.
