Recommendation · rule
I think it’s always useful to pay attention to the history on stuff like this and it’s a rare pleasure to be able to give some pointers in the literature along with some color to those interested from first-hand experience. I’d point the interested at the DLRM paper [1]: that was just after I left and I’m sad I missed it. FB got into disagg racks and SDN and stuff fairly early, and we already had half-U dual-socket SKUs with the SSD and (increasingly) even DRAM elsewhere in the rack in 2018, but we were doing huge NNs for recommenders and rankers even for then. I don’t know if this is considered proprietary so I’ll play it safe and just say that a click-prediction model…
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on IG Stories in 2018 was on the order of a modest but real LLM today (at FP32!). The crazy part is they were HOGWILD trained on Intel AVX-2, which is just wild to think about. When I was screwing around with CUDA kernels we were time sharing NVIDIA dev boxes, typically 2-4 people doing CUDA were splitting up a single card as late as maybe 2016. I was managing what was called “IGML Infra” when I left and was on a first-name basis with the next-gen hardware people and any NVIDIA deal was still so closely guarded I didn’t hear more than rumors about GPUs for training let alone inference. 350k Hopper this year, Jesus. Say what you want about Meta but don’t say they can’t pour concrete and design SKUs on a dime: best damned infrastructure folks in the game pound-for-pound to this day. The talk by Thomas “tnb” Bredillet in particular I’d recommend: one of the finest hackers, mathematicians, and humans I’ve ever had the pleasure to know. [1] https://arxiv.org/pdf/1906.00091.pdf [2] https://arxiv.org/pdf/2108.09373.pdf [3] https://engineering.fb.com/2022/10/18/open-source/ocp-summit... [4] https://youtu.be/lQlIwWVlPGo?si=rRbRUAXX7aM0UcVO