top | item 10489189 (no title) igul222 | 10 years ago This is also not quite right. Models whose parameters are too big to fit on one GPU can be trained by splitting them across multiple GPUs, as was done here, for example: http://papers.nips.cc/paper/5346-sequence-to-sequence-learni... discuss order hn newest brianchu|10 years ago According to the paper, the parameters fit on one GPU (or at least that one GPU was able to train the model). It was just too slow, so they trained on 8 GPUs in parallel. But those GPUs were still on the same machine (one node, multiple GPUs).
brianchu|10 years ago According to the paper, the parameters fit on one GPU (or at least that one GPU was able to train the model). It was just too slow, so they trained on 8 GPUs in parallel. But those GPUs were still on the same machine (one node, multiple GPUs).
brianchu|10 years ago