From the Meta post: "This chip’s architecture is fundamentally focused on providing the right balance of compute, memory bandwidth, and memory capacity for serving ranking and recommendation models."
Optimizing for ranking/recommendation models is very different from general purpose training/inference.
LPDDR5 vs HBMe2. I'm guessing there's a 2-5x price difference between those, but even so it's an interesting choice, I don't know any other accelerators which spec DDR. But yeah, without exact TCO numbers it's hard to compare exactly.
If the bandwidth capability of DDR suffices, HBM isn't worth it.
At least with LPDDR's; GDDRs may well not be worth it under data center TCO considerations due to the high interface power usage. Feel free to correct me if I'm mistaken, the numbers in question aren't too easy to search for so I didn't confirm this (LPDDR vs. GDDR) part.
Can't imagine any other reason other than cost as to why they went with LPDDR5, LPDDR5X has more bandwidth and GDDR6 has even more.
And, they mention a compiler in PyTorch, is that open sourced? I really liked the Google Coral chips -- they are perfect little chips for running image recognition and bounding box tasks. But since the compiler is closed source it's impossible to extend them for anything else beyond what Google had in mind for them when they came out in 2018, and they are completely tied to Tensorflow, with a very risky software support story going forward (it's a google product after all).
Is it the same story for this chip?
Still this looks like it would make for an amazing prosumer home ai setup. Could probably fit 12 accelerators on a wall outlet with change for a cpu, would have enough memory to serve a 2T model at 4bit and reasonable dense performance for small training runs and image stuff. Potentially not costing too much to make either without having to pay for cowos or hbm.
I'd definitely buy one if they ever decided to sell it and could keep the price under like $800/accelerator.
Glad someone was thinking the same thing I was though!
Wishful thinking maybe they'll announce selling it with the giant llama3 cause there's no good, cheap way to inference something like that at home at the moment and this could change that.
I can only imagine the lack of fear Jensen experiences when reading this.
I assume this helps reduce their server and electricity costs. At a certain scale these things pay off.
Low power 25W
Could use higher bandwidth memory if their workloads were more than recommendation engines.
Still relatively low compared to GPUs.
I saw this YC startup ad right after I finished reading this.
I feel like Zuck figured out he’s just running an ads network, the world is a long way anway from some VR fever dream, and to focus on milking each DAU for as many clicks as possible.
