31–40 of 112 posts
I'm a bit baffled. How high are the tuitions at the university of florida? This supercomputer is more powerful than many at the national labs and must cost a fortune (multiple 100 millions of dollars).
Does anyone know why everyone is still buying Nvidia instead of custom AI accelerators from other vendors? For example, on paper the new Graphcore machines look like an easy win, or at least a risk worth taking. (I see this particular supercomputer was funded by Nvidia but my question is about the general trend).
That's a lot of factors to keep in mind when you're spending millions on a supercomputer. I'd go for an established hardware provider as well. I'm not knocking the custom AI chips, but I wouldn't try and max out my budget with those - keep them for smaller applications for now.
Does anyone know why everyone is still buying Nvidia instead of custom AI accelerators from other vendors? For example, on paper the new Graphcore machines look like an easy win, or at least a risk worth taking. (I see this particular supercomputer was funded by Nvidia but my question is about the general trend).
Does anyone know why everyone is still buying Nvidia instead of custom AI accelerators from other vendors? For example, on paper the new Graphcore machines look like an easy win, or at least a risk worth taking. (I see this particular supercomputer was funded by Nvidia but my question is about the general trend).
Risk mitigation, maintenance, resell value (?), support, reliability. The custom AI accelerators you mentioned, how long have those been in business for? How many units have they moved? How many generations of hardware have they produced? Will they still be around to replace or upgrade units in 5-10 years? How flexible are they in their workload? That's a lot of factors to keep in mind when you're spending millions o…
What matters is the software platform and compatibility: you don’t want to retool and port all your existing programs. That’s where having a platform - CUDA - becomes a deal-maker.
What I don’t understand is how consumers - big, institutional consumers- don’t insist on viable vendor neutral platforms like OpenCL. Obviously they’re the only ones that have an interest in cross-vendor compatibility.
Earlier quoted context omitted.
Risk mitigation, maintenance, resell value (?), support, reliability. The custom AI accelerators you mentioned, how long have those been in business for? How many units have they moved? How many generations of hardware have they produced? Will they still be around to replace or upgrade units in 5-10 years? How flexible are they in their workload? That's a lot of factors to keep in mind when you're spending millions o…
Your supercomputer won’t last more than a couple years, 5 at max. Then it will simply be unjustifiably expensive to run when newer, more power efficient hardware becomes available. What matters is the software platform and compatibility: you don’t want to retool and port all your existing programs. That’s where having a platform - CUDA - becomes a deal-maker. What I don’t understand is how consumers - big, institutio…
The same reason a big institutional consumer will happily pay Microsoft for Windows Enterprise, instead of demanding Microsoft rebuild Windows using open source technologies.
A supercomputer or DC cluster has a fixed lifespan and amortization period, if you can get something in a contract, with everything you want for a price you are happy with, why would you introduce risk to it by demanding open standards for no benefit?
Earlier quoted context omitted.
I'm in favor of getting high powered compute in everyone's hands, so this is great to me.
Why? I mean, we already have (my old 2013 desktop would - I guess - compete with a cray 1 from the 1980s). What do you expect will happen if people get more CPU?
Does anyone know why everyone is still buying Nvidia instead of custom AI accelerators from other vendors? For example, on paper the new Graphcore machines look like an easy win, or at least a risk worth taking. (I see this particular supercomputer was funded by Nvidia but my question is about the general trend).
I'm a bit baffled. How high are the tuitions at the university of florida? This supercomputer is more powerful than many at the national labs and must cost a fortune (multiple 100 millions of dollars).