Live data from Hacker News

Unified Memory, Explained: Why Mini PCs Can Run 70B Models a Big GPU Can't

vettedconsumer.com

31–40 of 62 posts

Re: Unified Memory, Explained: Why Mini PCs Can Run 70B Models a Big GPU Can't

#31

Earlier quoted context omitted.

I was under the impression that when you're streaming the weights from disk because the full model won't fit in memory, that it is solely reading from the SSD, not writing, so it wouldn't be causing wear on your SSD.

NAND[0] has a fun thing called "read disturbance" where repeated reads from disk will eventually flip 0s to 1s. You have to erase and rewrite the block before the bits flip[1], or you lose the data, but doing so is the same amount of wear as a write. [0] I heard this being an issue with TLC, I don't know if it also applied to MLC or SLC. [1] I suspect in practice they use an error correction code and rewrite blocks t…

It's kinda irresponsible to talk about read disturbance without clarifying that it takes an extremely large number of reads to cause a read disturb error, and it can be corrected by a single rewrite of the data. Read disturb errors are something SSD engineers need to account for, but from an end user perspective it's a smaller problem by multiple orders of magnitude than write endurance, which is already rarely a real problem in practice.

Re: Unified Memory, Explained: Why Mini PCs Can Run 70B Models a Big GPU Can't

#32
post #29

Earlier quoted context omitted.

An old ada Rtx 6000 maybe. A Blackwell RTX Pro 6000 is an order of magnitude faster and has 96gb.

That's not what I'm seeing. It is much faster but not an order of magnitude. Not trying to be pedantic, only setting expectations. "The Blackwell RTX PRO 6000 provides up to 1,792 GB/s of memory bandwidth, while the 40-core Apple M5 Max tops out at 614 GB/s"

Sorry, thought we were talking about tokens. M5 Max is great for bandwidth and I’m looking forward to seeing what Apple does for AI inference in the M7. The 6000 kills everything else when it comes to TTFT and tokens/s.

Re: Unified Memory, Explained: Why Mini PCs Can Run 70B Models a Big GPU Can't

#33

If compute is not the bottleneck, memory is easy-ish to produce (the hard part is mostly on the fab side); what stops a Chinese NVIDIA (huawei) from being 10x cheaper?

I think it's mostly the ramp-up time, but ChangXin Memory Technologies (CXMT) is basically aspiring to do just this.

Re: Unified Memory, Explained: Why Mini PCs Can Run 70B Models a Big GPU Can't

#36

If compute is not the bottleneck, memory is easy-ish to produce (the hard part is mostly on the fab side); what stops a Chinese NVIDIA (huawei) from being 10x cheaper?

Making memory is easy. Packaging that memory within a few millimeters of a piece of silicon using TSVs and maintaining signal integrity on a 1024 bit bus is really, really hard.

LLMs aren’t all that compute constrained or even memory constrained. It’s just that pushing dozens of terabits per second through a piece of silicon is a physics problem.

Re: Unified Memory, Explained: Why Mini PCs Can Run 70B Models a Big GPU Can't

#37

Let's also ensure the SSD doesn't age prematurely.

I was under the impression that when you're streaming the weights from disk because the full model won't fit in memory, that it is solely reading from the SSD, not writing, so it wouldn't be causing wear on your SSD.

You'd need your OS to support, and be configured to use, a disk mounting option that disables file access timestamps, otherwise reads ARE writes.

Re: Unified Memory, Explained: Why Mini PCs Can Run 70B Models a Big GPU Can't

#38
post #29

Earlier quoted context omitted.

That's not what I'm seeing. It is much faster but not an order of magnitude. Not trying to be pedantic, only setting expectations. "The Blackwell RTX PRO 6000 provides up to 1,792 GB/s of memory bandwidth, while the 40-core Apple M5 Max tops out at 614 GB/s"

Sorry, thought we were talking about tokens. M5 Max is great for bandwidth and I’m looking forward to seeing what Apple does for AI inference in the M7. The 6000 kills everything else when it comes to TTFT and tokens/s.

For sure. Clearly Nvidia mops the floor with the competition. I'm looking forward to M6/M7 and to see if Apple wants a bigger piece of the pie.

Re: Unified Memory, Explained: Why Mini PCs Can Run 70B Models a Big GPU Can't

#39
Why would a RTX 5090 with 32 GB not be able to deal with a 40 GB model? Is there anything preventing me from swapping the weights that do not fit into VRAM in and out of RAM? PCIe 5.0 x16 should max out around 64 GB/s, so slower than the unified memory machine, but at least it should be possible.

Re: Unified Memory, Explained: Why Mini PCs Can Run 70B Models a Big GPU Can't

#40
post #39

Why would a RTX 5090 with 32 GB not be able to deal with a 40 GB model? Is there anything preventing me from swapping the weights that do not fit into VRAM in and out of RAM? PCIe 5.0 x16 should max out around 64 GB/s, so slower than the unified memory machine, but at least it should be possible.

It's slower than the 4:1 ratio would imply, but it does indeed work.

Things get really slow if the model doesn't for in vram + ram and you have to go from disk to ram to vram.

Post reply on HN