Re:YaLM-100B: Pretrained language model with 100B parameters
by justinlloyd on Thursday June 23, 2022 @01:51PM
(#31852735)
You don't need a "decent amount" of PCIe-4 lanes. You just need 16 of them. And they can be PCIe 3.0 and will work just fine. Deep learning compute boxes predominantly use a PCIe switch. e.g. the ASUS 8000 box, which handles eight cards just fine. You only need a metric tonne of PCIe bandwidth if you are constantly shuttling data in and out of the GPU, e.g. in a game or exceedinyl large training sets of computer vision data. A little latency of a few hundred milliseconds moving data to your GPU in a training session that will take hours if not days to complete is neither here nor then. I suspect this model, with a little tweaking, will run just fine on an eight way RTX A5000 setup, or a five-way A6000 completely unhindered. That puts the price around $20,000 to $30,000. If I put two more A5000s in my machine, I suspect I could figure out how to get the model to load.
It also sounds like they haven't optimized their model, or done any split on it, but if they did, I suspect they could load it up and have it infer slower on fewer GPUs, by using main memory.
It also sounds like they haven't optimized their model, or done any split on it, but if they did, I suspect they could load it up and have it infer slower on fewer GPUs, by using main memory.