Looking at something like https://lambdalabs.com/deep-learning/gpu-benchmarks or https://github.com/tensorpack/benchmarks/tree/master/other-w..., multi-gpu scaling on 2080tis seems pretty darn close to linear. Plus, there are benefits to having more than one accelerator handy on a local workstation. For one, it's much easier to have multiple experiments running simultaneously or to run parallel training (e.g. hyperparameter search or RL episodes). Given that only the uber-expensive enterprise cards have proper virtualization/time sharing, trying this workflow on a Titan RTX will most likely be suboptimal unless you always run models that can make use of most of the memory and compute (no RNNs, no Neural ODEs, etc.)