No. But it does potentially mean that either current or future-tweaked GPUs could run a lot more efficiently -- meaning much faster or with much less energy consumption.
The bottleneck on a consumer-grade GPU like a 3090 isn't the processing power, it's the lack of RAM. The PCI-Express bus ends up being your bottleneck from having to swap in parts of the model.
Even with PCIe 5.0 and 16 lanes, you only get 64 GB/s of bandwidth. If you're trying to run a model too big for your GPU, then for every token, it has to reload the entire model. With a 70B parameter model, 8 bit quantization, you're looking at just under 1 token/sec just from having to transfer parts of the model in constantly. Making the actual computation faster won't make it any faster.
OTOH, doesn't it also mean that (given appropriate software framework support) iGPUs with less processing capacity and slower-but-more RAM available (because system RAM is comparatively cheap and plentiful compared to VRAM) without swapping anything are more competitive against consumer dGPUs with fast-but-small RAM for both inference and training with larger models?
System memory isn't that fast, either. Even with DDR5-8400, the fastest memory you can get right now, you're only looking at a memory transfer speed of 67.2 GB/s, barely faster than the PCI-E bus. So even if you could store that entire 70B model in RAM, you're still getting just under 1 token/sec, and that's assuming your CPU doesn't become a bottleneck.
Your best bet would likely be a laptop that has integrated system RAM with VRAM, but I don't think any of those offer enough RAM to store an entire 70B model. A 7B parameter model would work fine, but you could do those on a consumer-grade GPU anyways.
I had a feeling it had to be something like massive waste due to a misguided feature of the algorithms that shouldn't have been there in the first place.
Once the "math is done" quite likely it would have paid off better than most investments for the top people to have spent a few short years working with grossly underpowered hardware until they could come up with amazing results there before scaling up. Rather than grossly overpowered hardware before there was even deep understanding of the underlying processes.
When you think about it, what we have seen from the latest ultra-high-powered "thinking" machines is truly so impressive. But if you are trying to fool somebody into believing that it's a real person it's still not "quite" there.
Maybe a good benchmark would be to take a regular PC, and without reliance on AI just pull out all the stops and put all the effort into fakery itself. No holds barred, any trick you can think of. See what the electronics is capable of this way. There are some smart engineers, this would only take a few years but looks like it would have been a lot more affordable.
Then with the same hardware if an AI alternative is not as convincing, something has got to be wrong.
It's good to find out this type of thing before you go overboard.
Regardless of speed or power, I never could have gotten an 8-bit computer to match the output of a 32-bit floating-point algorithm by using floating-point myself. Integers all the way and place the decimal where it's supposed to be when you're done.
Once it's really figured out, how do you think it would feel being the one paying the electric bills up until now?
Faster progress was absolutely worth it. Spending years agonizing over theory to save a bit of electric would have been a massive disservice to the world.
I've been using copilot for several months. If I could figure out a way to measure its impact on my productivity, I'd probably see a single digit percentage boost in "productivity". This is not life-changing for me. And for some tasks, it's actually worse than nothing. As in, I spend time feeding it a task, and it just completely fails to do anything useful.
I've been using it for over a year I think. I don't often feed it tasks with comments so much as go about things the same as usual and let it autocomplete. The time and cognitive load saved adds up massively. I've had to go without it for a bit while my workplace gets its license in order for the corporate version and the personal version has an issue with the proxy, and it's been agonizing going without it again. I almost forgot how much it sucks having to jump to google every other minute, and it was easy to start to take for granted how much context copilot was letting me not have to hold onto in my head. It really lets me work on the problem as opposed to being mired in immaterial details. It feels like I'm at least 2x slower overall without it.
If you're already a competent developer, I think that's a reasonable expectation of impact on productivity. I think the 'life-changing' part comes in helping someone get to the point of building things with code where before they couldn't (or believed they couldn't). It does a lot better job of turning the enthusiasts and code-curious into amateurs vs. empowering professionals.
If you are in maintenance mode your visits to Copilot will be rare. If you are building greenfield, use goes through the roof. All those test cases, nevermind all the POC and framework scaffolding and other boilerplate that is now completely unacceptable as a use of developer time.
"Even just in programming" the jury is still out. None of my coworkers using these are noticeably more productive than the ones who don't. Outside of programming no one gives a shit except scammers and hype chasers.
The people writing articles for journals that aggregate and approximate other sources are in mortal terror of LLMs. Likewise graphic designers and anyone working in (human language) translation.
I don't fear that LLMs are going to take my job as a developer. I'm pretty sure they mark a further decrease in the quality and coherence of software, along with a rapid increase in the quantity of code out there, and that seems likely to provide me with reliable employment forever. I'm basically employed in fixing bugs that didn't need to exist in the first place and that seems to cover a lot of software dev.
Are you sure it’s a life changing productivity booster? Sometimes I look at my projects and wonder how would I explain it to an LLM what this code should have done if it didn’t exist yet. Must be a shitton of boilerplate programming for copilot to be a life-changing experience.
And here you're assuming that making software engineers more productive would be a service to the world. I think the jury is out on that one as well. At least for the majority of software engineering since 2010.
Yes, a large amount for - in the grand scheme of things - a short period of time (i.e., a quantity of energy usage in an intense spike that will be dwarfed by energy usage over time) can accurately be described as “a bit”.
Of course, the impact is that AI will continue to become cheaper to use, and induced demand will continue the feedback loop driving the market as a result.
This comment lives in a fictional world where there is a singular group that could have collectively acted counterfactually. In the real world any actor that individually went this route would have gone bankrupt while the others collected money by showing actual results even if ineffeciently earned.
Also it is likely that the rise of LLMs gave many researchers in allied fields the impetus to tackle with the problems that are relevant to making it more efficient and people stumbled upon a solution hiding there.
The momentum with LLMs and allied technology may last till it keeps on improving even by a few percentage points and keeps shattering human created new benchmarks every few months
That is a good correlation when you think about how much more energy-efficient transistors are than vacuum tubes.
Vacuum tube computers were a thing for a while, but it was more out of desperation than systematic intellectual progress.
OTOH you could look at the present accomplishments like it was throwing more vacuum tubes at a problem that can not be adequately addressed that way.
What turned out to be a solid-state solution was a completely different approach from the ground up.
To the extent a more power-saving technique using the same hardware is only a matter of different software approaches, that would be something that realistically could have been accomplished before so much energy was expended.
Even though I've always thought application-specific circuits would be what really helps ML and AI a lot, and that would end up not being the exact same hardware at all.
If power is truly being wasted enough to start rearing its ugly head, somebody should be able to figure out how to fix it before it gets out-of-hand.
Ironically enough with my experience using vacuum tubes, I've felt that there were some serious losses in technology when the research momentum involved was so rapidly abandoned in favor of "solid-state everything" at any cost.
Maybe it is a good idea to abandon the energy-intensive approaches, as soon as anything completely different that's the least bit promising can barely be seen by a gifted visionary to have a glimmer of potential.
Its iteritive, there are plenty of cul-de-sacs and failures. You can't really optimise until you have something that works and its a messy process that is inefficient.
Isn’t this paper pretty much about spending a few short years to improve the performance? Or are you arguing that the same people who made breakthroughs over the last few years should have also done the optimization work?
>the same people who made breakthroughs over the last few years should have also done the optimization work
I never thought it would be ideal if it was otherwise, so I guess so.
When I first considered neural nets from state-of-the art vendors to assist with some non-linguistic situations over 30 years ago, it wasn't quite ready for prime time and I could accept that.
I just don't have generic situations all the time which would benefit me, so it's clearly my problems that have the deficiencies ;\
What's being done now with all the resources being thrown at it is highly impressive, and gaining all the time, no doubt about it. It's nice to know there are people that can afford it.
I truly look forward to more progress, and this may be the previously unreached milestone I have been detecting that might be a big one.
Still not good enough for what I need yet so far though. And I can accept that as easily as ever.
That's why I put up my estimation that not all of those 30+ years has been spent without agonizing over something ;)