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> the big risk for NVDA is that models and tools get small and efficient enough that they don't need $1,000+ hardware. You will always be able to do more and b
by jeffparsons 4y ago
> the big risk for NVDA is that models and tools get small and efficient enough that they don't need $1,000+ hardware.
You will always be able to do more and better with the premium hardware. Maybe for video games the marginal differences aren't enough to sway most consumers towards the high end cards, but I expect there will be a lot of people willing to pay top dollar to be able to run the best version of X AI tool.
- solardev 4y agoWith games, the best card might make sense cuz it could be the difference between 60+ fps and 40. For workloads though, does it really matter if it completes in 20 seconds instead of 15? Unless there's some feature difference in the higher end cards (like AI-specific subchips), not just more of the same & faster, then a lower card of the same generation shouldn't impact your ability to run something or not. Maybe RAM size...
- squeaky-clean 4y agoHow many times do you have to complete that 20/15 second task in a year? Multiply that by 5 seconds to get the "wasted time". Factor in the employees salary by that wasted time, how much are you paying them to wait on that slower hardware? Is it more money than the price difference between the better GPU and the cheaper GPU? Or maybe it's an automated thing that runs constantly? The difference between 20 and 15 seconds is 480 additional iterations per 8-hours. How much is that worth to you? There's also the unmeasurable benefits. What if you're losing potential good employees applying because word on the net is your company uses last-gen hardware for everything, and your competitors use the newest.
- solardev 4y agoI don't think it's that binary a thing. It's not like either you have a supercomputer or you have last decade's junk. There are so many different models and price points, each company/team/dev can find their own sweet spot and upgrade cadence. It's not necessarily the case that 20% better performance is 20% more money, and 5 seconds times like 100 times per week times a year is still just a single workday. I doubt any employee works effectively 365 days a year... and the cost difference of the highest end card vs a medium one is definitely more than I get paid in a day, at least. I mean, it's no different than CPUs and RAM and such. A middle-of-the-road laptop from the last year or two is good enough for most users... no company I've ever worked for gets the top-of-the-line model every single refresh for every single dev. That'd be an absurd waste of money, IMO. Personally, I wouldn't want to work for a company that upgrades hardware all the time for single-digit performance increases. That money is better used elsewhere, whether employee salaries/benefits or donating it to the community or just saving customers some money or paying dividends to shareholders. Even if you're running massive continuous workloads, it is probably more cost-efficient to just parallelize a bunch of midrange systems (optimize for perf/dollars and/or perf/watt) than to just chase the highest-end chips for no reason.
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- Blammar 4y agoRAM size is the issue. I can't run stable diffusion at 1k x 1k on my 2080ti because I run out of vram.
- bitL 4y agoThe new 40x0 series has no NVlink even on their pro cards, which is a major letdown and step back for AI folks.