![]() If you’re basing a buying decision on the possibility that TitanV TC performance might be 10% higher than Titan RTX according to some measurement, then go for it. I personally don’t think Tensor Core is a reason to choose TitanV (over Titan RTX), and I’m not sure why you reached that conclusion. 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. Turing TensorCore capability varies substantially based on calculation mode.They are generally not achievable using real-world benchmarking. The numbers indicated above are peak theoretical calculated numbers for relative comparison purposes only. You should independently confirm any expected/desired characteristics prior to making any buying decisions. This is not a statement of specification from NVIDIA. The data presented here are assembled from other internet resources and may contain errors. TCFP: * 110 TFlop/s mixed precision TensorCore throughput Titan RTX Titan VįP64: 0.5 7.5 TFlop/s (approximate - based on boost clock)įP32: 16 15 TFlop/s (approximate - based on boost clock)įP16: 32 30 TFlop/s (approximate - based on boost clock) Top level specs comparison (peak theoretical). In that case, Titan RTX is the clear win.) (Another clear differentiator would be if you are interested in ray-tracing performance, using either a industry ray-tracing API, or Optix. In most other respects the two cards should be roughly comparable. And as you’ve already pointed out, Titan RTX, at 24GB, has twice the memory of Titan V. Unless you are doing a lot of double-precision matrix multiply work, that may not be a likely description of your use case. TitanV will be a clear win when your application is bound by FP64 throughput.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |