If your work involves modern-API-based raytracing, there’s really no comparison between the two cards. If your code must run a single deep learning training scenario on a single GPU, and the model will not fit in 12GB (but will fit in 24GB), there’s really no comparison between the two cards. If your code does large amounts of FP64 matrix multiply, there’s really no comparison between the two cards. These are arguably all capability differentiators. The ones I listed were: FP64, memory size, and Ray Tracing. I didn’t list TensorCore in my top-level list of differentiators. It seems that the only reason to choose the Titan V is for FP64 support and Tensor Cores. This is why it is important to benchmark one’s actual applications if one desires to maximize their performance. I haven’t looked how much variation their is between code generated for Volta vs code generated for Turing, but would expect some differences as GPU architectures are not binary compatible and code generators are re-tweaked for new architectures. Performance is also influenced by the code generated by the compiler. Especially when the hardware specs are close, it is not possible to say where in that spectrum a given application will show up. The way I look at it is that the theoretical memory bandwidth specifications suggest that the Titan RTX will be slightly faster than the Titan V on many memory-bound applications, but one could also encounter cases where it is slower than a Titan V. FWIW the Titan RTX uses GDDR6, which presumably enables higher capacity at lower cost (note the “G” for regular DDR we are still at DDR4 at this time). Practical performance of any kind of DRAM will also depend on the specifics of the memory controller, and NVIDIA does not make those available. Sorry, my in-depth understanding of DRAM technology stopped at early DDR devices. For description of other modes, refer to the turing white paper blog: Base-level mode (FP16 precision) should be comparable to Volta TC per unit at constant clocks.
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