I agree with CameronDev, not so much on the capacity, but the bandwidth. At 100+ Gb, the Ryzen/Core platforms are really holding you back with their weak I/O.
If you need that much memory, you might be better off picking up a used Xeon/Epyc from Ebay. Their CPU speeds are lower, but the quad channel RAM could make up for it, depending on what you’re trying to do.
I’d say this is the correct answer. If you’re actually using that much RAM, you probably want it connected to the processor with a wide (fast) bus. I rarely see people do it with desktop or gaming processors. It might be useful for some edge-cases, but usually you want an Epyc processor or something like that, or it’s way too much RAM that isn’t connected fast enough.
My edge case is: I wanna spin up an ai-lxc in proxmox. ollama and open webui. using RAM instead of vram. but it should low on power consumption on idle. thats why I want an intel i-9 oder core ultra 9 with maxed out RAM. it idles on low power, but can run bigger ai-models using RAM instead of VRAM. it would be not so fast like with GPUs, but thats OK.
I think a xeon would need more power…much more power in idle. I have an old Xeon E3-1275 v5, 32 GB RAM with a supermicro D3417-B mainboard and it idles about 10 Watts. this is fantastic, I but I don’t think I can get a good newer Xeon with low consumption like this. but I wanna send the old lady to retirement.
AI inference is memory-bound. So, memory bus width is the main bottleneck. I also do AI on an (old) CPU, but the CPU itself is mainly idle and waiting for the memory. I’d say it’ll likely be very slow, like waiting 10 minutes for a longer answer. I believe all the AI people use Apple silicon because of the unified memory and it’s bus width. Or some CPU with multiple memory channels. The CPU speed doesn’t really matter, you could choose a way slower one, because the actual multiplications aren’t what slows it down. But you seem to be doing the opposite, get a very fast processor with just 2 memory channels.
Well, the numbers I find on google are: a Nvidia 4090 can transfer 1008 GB/s. And a i9 does something like 90 GB/s. So you’d expect the CPU to be roughly 11 times slower than that GPU at fetching an enormous amount of numbers from memory.
I think if you double the amount of DDR channels for your CPU, and if that also meant your transfer rate would double to 180 GB/s, you’d just be roughly 6 times slower than the 4090. I’m not sure if it works exactly like that. But I’d guess so. And there doesn’t seem to be a recent i9 with quad channel. So you’re stuck with a small fraction of the speed of a GPU if you’re set on an i9. That’s why I mentioned AMD Epyc or Apple processors. Those have a way higher memory throughput.
And a larger model also means more numbers to transfer. So if you now also use your larger memory to use a 70B parameter model instead of an 12B parameter model (or whatever fits on a GPU), your tokens will now come in at a 65th of the speed in the end. Or phrased differently: you don’t wait 6 seconds, but 6 and a half minutes.
The i9-10900 has 4 channels (Quadro-Channel DDR4-2933 (PC4-23466, 93.9GB/s).
would this be better in this way than an i9-14xxx (Dual-Channel DDR5-5600 (PC5-44800, 89.6GB/s))?
does the numbers (93 GB/s and 89GB/s) mean the speed for a RAM-stick or the speed all together? maybe an old i9-10xxx with 4channel-ram was better than a new dual-channel.
Seems it means all together. (5600MT/s / 1000) x 2 sticks simultaneously x 64bit / 8bits/Byte = 89.6 GB/s
or 2933/1000 x 4 x 64bit / 8 = 93.9 GB/s
so they calculated with double the DDR bus width in the one example, and 4 times the bus width in the other one. That means dual or quad channel is already factored in in those numbers. And yes, the old one seems to be slightly better than the new one. At least regarding memory throughput. I suppose everything else has been improved on. And you need to put in 4 correct RAM sticks to make use of it in the first place.
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I agree with CameronDev, not so much on the capacity, but the bandwidth. At 100+ Gb, the Ryzen/Core platforms are really holding you back with their weak I/O.
If you need that much memory, you might be better off picking up a used Xeon/Epyc from Ebay. Their CPU speeds are lower, but the quad channel RAM could make up for it, depending on what you’re trying to do.
I’d say this is the correct answer. If you’re actually using that much RAM, you probably want it connected to the processor with a wide (fast) bus. I rarely see people do it with desktop or gaming processors. It might be useful for some edge-cases, but usually you want an Epyc processor or something like that, or it’s way too much RAM that isn’t connected fast enough.
My edge case is: I wanna spin up an ai-lxc in proxmox. ollama and open webui. using RAM instead of vram. but it should low on power consumption on idle. thats why I want an intel i-9 oder core ultra 9 with maxed out RAM. it idles on low power, but can run bigger ai-models using RAM instead of VRAM. it would be not so fast like with GPUs, but thats OK.
I think a xeon would need more power…much more power in idle. I have an old Xeon E3-1275 v5, 32 GB RAM with a supermicro D3417-B mainboard and it idles about 10 Watts. this is fantastic, I but I don’t think I can get a good newer Xeon with low consumption like this. but I wanna send the old lady to retirement.
AI inference is memory-bound. So, memory bus width is the main bottleneck. I also do AI on an (old) CPU, but the CPU itself is mainly idle and waiting for the memory. I’d say it’ll likely be very slow, like waiting 10 minutes for a longer answer. I believe all the AI people use Apple silicon because of the unified memory and it’s bus width. Or some CPU with multiple memory channels. The CPU speed doesn’t really matter, you could choose a way slower one, because the actual multiplications aren’t what slows it down. But you seem to be doing the opposite, get a very fast processor with just 2 memory channels.
So if I had more memory channels it would be better to have say ollama use the cpu versus the gpu?
Well, the numbers I find on google are: a Nvidia 4090 can transfer 1008 GB/s. And a i9 does something like 90 GB/s. So you’d expect the CPU to be roughly 11 times slower than that GPU at fetching an enormous amount of numbers from memory.
I think if you double the amount of DDR channels for your CPU, and if that also meant your transfer rate would double to 180 GB/s, you’d just be roughly 6 times slower than the 4090. I’m not sure if it works exactly like that. But I’d guess so. And there doesn’t seem to be a recent i9 with quad channel. So you’re stuck with a small fraction of the speed of a GPU if you’re set on an i9. That’s why I mentioned AMD Epyc or Apple processors. Those have a way higher memory throughput.
And a larger model also means more numbers to transfer. So if you now also use your larger memory to use a 70B parameter model instead of an 12B parameter model (or whatever fits on a GPU), your tokens will now come in at a 65th of the speed in the end. Or phrased differently: you don’t wait 6 seconds, but 6 and a half minutes.
The i9-10900 has 4 channels (Quadro-Channel DDR4-2933 (PC4-23466, 93.9GB/s). would this be better in this way than an i9-14xxx (Dual-Channel DDR5-5600 (PC5-44800, 89.6GB/s))?
does the numbers (93 GB/s and 89GB/s) mean the speed for a RAM-stick or the speed all together? maybe an old i9-10xxx with 4channel-ram was better than a new dual-channel.
Seems it means all together. (5600MT/s / 1000) x 2 sticks simultaneously x 64bit / 8bits/Byte = 89.6 GB/s
or 2933/1000 x 4 x 64bit / 8 = 93.9 GB/s
so they calculated with double the DDR bus width in the one example, and 4 times the bus width in the other one. That means dual or quad channel is already factored in in those numbers. And yes, the old one seems to be slightly better than the new one. At least regarding memory throughput. I suppose everything else has been improved on. And you need to put in 4 correct RAM sticks to make use of it in the first place.
Maybe check out this video?
He goes over the different ways to run a selfhost AI without a GPU, like you want to do, including maxing RAM and using PCI-e M.2 add-on boards.
Thank you very much! This leads to this article: https://forum.level1techs.com/t/deepseek-deep-dive-r1-at-home/225826/2 Maybe the 9959x is what I am looking for.