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Anyone have experience and suggestions for an AI accelerator? Think prototype consumer product with total cost preferably < $500, definitely less than $1000.
Anyone have experience and suggestions for an AI accelerator? Think prototype consumer product with total cost preferably < $500, definitely less than $1000.
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Does it not work for them? Where can I learn why?
Just go have a look around Github issues in their ROCm repositories on Github. A few months back the top excuse re: AMD was that we're not supposed to use their "consumer" cards, however the datacenter stuff is kosher. Well, guess what, we have purchased their datacenter card, MI50, and it's similarly screwed. Too many bugs in the kernel, kernel crashes, hangs, and the ROCm code is buggy / incomplete. When it works,…
> Twenty-four 200 gigabit (Gb) Ethernet ports are integrated into every Intel Gaudi 3 accelerator WHAT‽ It's basically got the equivalent of a 24-port, 200-gigabit switch built into it. How does that make sense? Can you imaging stringing 24 Cat 8 cables between servers in a single rack? Wait: How do you even decide where those cables go? Do you buy 24 Gaudi 3 accelerators and run cables directly between every single…
The amount of power that will use up is massive, they should've gone for some fiber instead
Wow, I very much appreciate the use of the 5 Ws and H [1] in this announcement. Thank you Intel for not subjecting my eyes to corp BS [1] https://en.wikipedia.org/wiki/Five_Ws
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200gb is not going to be using CAT, it will be fiber (or direct attached copper cable as noted by dogma1138) with a QSFP interface
It will most likely use copper QSFP56 cables since these interfaces are either used in inter rack or adjacent rack direct attachments or to the nearest switch. O.5-1.5/2m copper cables are easily available and cheap and 4-8m (and even longer) is also possible with copper but tends to be more expensive and harder to get by. Even 800gb is possible with copper cables these days but you’ll end up spending just as much if…
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Just go have a look around Github issues in their ROCm repositories on Github. A few months back the top excuse re: AMD was that we're not supposed to use their "consumer" cards, however the datacenter stuff is kosher. Well, guess what, we have purchased their datacenter card, MI50, and it's similarly screwed. Too many bugs in the kernel, kernel crashes, hangs, and the ROCm code is buggy / incomplete. When it works,…
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Does it not work for them? Where can I learn why?
Just go have a look around Github issues in their ROCm repositories on Github. A few months back the top excuse re: AMD was that we're not supposed to use their "consumer" cards, however the datacenter stuff is kosher. Well, guess what, we have purchased their datacenter card, MI50, and it's similarly screwed. Too many bugs in the kernel, kernel crashes, hangs, and the ROCm code is buggy / incomplete. When it works,…
Earlier quoted context omitted.
Just go have a look around Github issues in their ROCm repositories on Github. A few months back the top excuse re: AMD was that we're not supposed to use their "consumer" cards, however the datacenter stuff is kosher. Well, guess what, we have purchased their datacenter card, MI50, and it's similarly screwed. Too many bugs in the kernel, kernel crashes, hangs, and the ROCm code is buggy / incomplete. When it works,…
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AMD MI300x is 192GB.
Which would be impressive had it _actually_ worked for ML workloads.
Do you have any more evidence as to why these categorically don't work?