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…
Unified Memory, Explained: Why Mini PCs Can Run 70B Models a Big GPU Can't
31–40 of 62 posts
Re: Unified Memory, Explained: Why Mini PCs Can Run 70B Models a Big GPU Can't
#32Earlier 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"
Re: Unified Memory, Explained: Why Mini PCs Can Run 70B Models a Big GPU Can't
#33If 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?
Re: Unified Memory, Explained: Why Mini PCs Can Run 70B Models a Big GPU Can't
#34Re: Unified Memory, Explained: Why Mini PCs Can Run 70B Models a Big GPU Can't
#35Re: Unified Memory, Explained: Why Mini PCs Can Run 70B Models a Big GPU Can't
#36If 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?
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
#37Let'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.
Re: Unified Memory, Explained: Why Mini PCs Can Run 70B Models a Big GPU Can't
#38Earlier 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.
Re: Unified Memory, Explained: Why Mini PCs Can Run 70B Models a Big GPU Can't
#39Re: Unified Memory, Explained: Why Mini PCs Can Run 70B Models a Big GPU Can't
#40Why 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.
Things get really slow if the model doesn't for in vram + ram and you have to go from disk to ram to vram.