Yes, ultimately everyone is currently doing something which looks like synchronous data parallel training on the outside.
The linked PDF is very light on detail, but what results they do claim are about a 1.2bn parameter model. This is tiny; you don't need network-bound distributed training (ie, anything beyond a single datacenter class machine, or less if you're patient) to train a model that size. The comms requirements also scale with the model size, so I strongly suspect people hoping for embarrassingly-parallel-style scaling properties are going to be disappointed.
(They also appear to have, in part, reinvented parameter servers.)
The linked PDF is very light on detail, but what results they do claim are about a 1.2bn parameter model. This is tiny; you don't need network-bound distributed training (ie, anything beyond a single datacenter class machine, or less if you're patient) to train a model that size. The comms requirements also scale with the model size, so I strongly suspect people hoping for embarrassingly-parallel-style scaling properties are going to be disappointed.
(They also appear to have, in part, reinvented parameter servers.)