61–70 of 104 posts
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“ With machine learning, you spent most of the time copying memory between the CPU and GPU” - this is a sign that you are most likely doing it wrong. Yes, some operations are inherently bandwidth bound, but most important ones such as larger matrix multiplies (transformers) and convolutions are compute bound.
Sure, but compared to the 3080 I'd say that the main deal is the bigger RAM for copying reason than the increased core count.
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I want a gaming computer than won't limit my future ML learning. Are there any suggestions for that use case?
just make sure it's NVidia. whatever graphics card you want -- all their consumer cards will work great for deep learning. make sure your motherboard and processor support whatever the newest version of PCIe is -- a major factor with deep learning is bandwidth moving data on/off the GPU. AMD GPUs can theoretically be used for machine learning, but right now software support is lacking -- you will spent more time conf…
Nvidia came to dominate the market at a time when AMD wasn't making particularly competitive GPUs, but that isn't really the case anymore. For anything not so expensive that nobody is really going to buy it anyway, the current and expected (in less than a month) AMD GPUs are competitive on performance.
The result is that a lot of large customers, who see value in not being locked into a single supplier, are going to be pushing for frameworks that work across multiple vendors. And then you could plausibly be wasting your time learning Nvidia-specific technology which is about to become disfavored. So you might want to wait and see.
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Here's hoping for an A100 titan with un-nerfed FP64. The 3090 is twice as nerfed as previous generations, which were also bad at 1:32. Now it's 1:64 :(
The FP64 units are a separate addition that eat a lot of die space, right? I wouldn't use the word "nerf" for the tradeoff between having more SMs versus having more features in the SMs.
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just make sure it's NVidia. whatever graphics card you want -- all their consumer cards will work great for deep learning. make sure your motherboard and processor support whatever the newest version of PCIe is -- a major factor with deep learning is bandwidth moving data on/off the GPU. AMD GPUs can theoretically be used for machine learning, but right now software support is lacking -- you will spent more time conf…
This is also a reason why it might make sense to hold off unless you have some kind of time-sensitive project. Nvidia came to dominate the market at a time when AMD wasn't making particularly competitive GPUs, but that isn't really the case anymore. For anything not so expensive that nobody is really going to buy it anyway, the current and expected (in less than a month) AMD GPUs are competitive on performance. The r…
so the question is just -- when will it be very simple to install these packages for AMD GPUs, with enough mathematical operations implemented and optimized to let you do the things you want to do.
right now things sort of work, but it's definitely in a bleeding edge early adopter state. it's seemed like AMD is on the cusp of catching up for a couple years now, but it's taken longer than I expected.
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just make sure it's NVidia. whatever graphics card you want -- all their consumer cards will work great for deep learning. make sure your motherboard and processor support whatever the newest version of PCIe is -- a major factor with deep learning is bandwidth moving data on/off the GPU. AMD GPUs can theoretically be used for machine learning, but right now software support is lacking -- you will spent more time conf…
This is also a reason why it might make sense to hold off unless you have some kind of time-sensitive project. Nvidia came to dominate the market at a time when AMD wasn't making particularly competitive GPUs, but that isn't really the case anymore. For anything not so expensive that nobody is really going to buy it anyway, the current and expected (in less than a month) AMD GPUs are competitive on performance. The r…
Seems like a good speedup relative to the Titan, especially for the money. I’d be interested to see the performance relative to the 3080 though. There are obviously vram limitations with the 3080 but it would still be interesting to see the difference in raw compute performance. In games the 3090 only gives a 15% performance bump relative to the 3080. If that pattern holds for machine learning tasks there is probably…
There will be 20GB version of 3080 soon.
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100% utilization is a pretty huge assumption. And if you ARE actually running it that hard, you'd better budget for fairly frequent replacement cards.
At 50% utilization it beats the cloud in 2 months. 10% utilization, it beats the cloud within a year. If you're dabbling, definitely go with the cloud, but if you're turning around experiments on a regular basis, buying gets attractive quickly. And no, cards don't just keel over in a few months at 100%. Crypto miners ran that experiment. A typical card has years of 100% in it.
I thought I read Nvidia was nerfing the GeForce cards. Does this disprove it?