I suspect this is a just-so-tale because the capability is so new and the GPU programming skill required to implement it efficiently is so rare. TLDR: just say

I suspect this is a just-so-tale because the capability is so new and the GPU programming skill required to implement it efficiently is so rare. TLDR: just say no to parameter server (most of the time). But IMO the real obstacle to horizontal scaling is the communication between servers not the usefulness of doing so. Within a server, one can pack 8 TitanX/M40 GPUs with high speed 13 GB/s P2P communication between them (and up to 16 GPUs in various unproven science project servers). That's ~50 TFLOPs and 96 GB in a box. That rocks. Just ask the guys at mindori (if they ever ship that is). http://www.mindori.com http://www.mindori.com But between servers lies a sippy straw of 100 Gb/s Infiniband at best or worst-case, ~1 Gb/s on AWS GPU servers with freaky nearest neighbor weather. If you can't make efficient use of 8 GPUs in a single box, I agree, don't bother breaking out to the next server. That said, frameworks like mxnet have opened the floodgates to experimenting with larger models and more distributed training algorithms. Time will tell if this pans out. But 2 years ago, Andrew Ng's group showed 12 GTX 680s distributed across 3 servers kicking the crap out of Google Brain. I expect more of this, not less, in the near future.