Deep Learning GPU Benchmarks: GTX 1080 vs. Titan X Pascal

It makes me sad that stacked bar graphs are even an option here.

They're using different CPUs[0], different amounts of RAM, different base OSes, different drivers and even one different product version (Tensorflow RC). As far as benchmarks go this is pretty awful. It does show some useful top-level comparisons, but it's about as un-rigorous as you can get.

[0] http://ark.intel.com/compare/82930,94188,77780

The test setup for the 1080 also had half the RAM of the others, and used an older ubuntu.

Not a great test of GPUs in isolation.

I would assume that it definitely affects the result if the procedure involves moving data between GPU and main memory, but I do not know the size of the effect.

I am still pretty confident that the difference in performance comes mostly from differences between different GPU architectures.

You're also paying for the Titan XPs 12GB of VRAM, which is overlooked by the common canned benchmarks that easily fit in the 1080s 8GB.

SLI is not for compute. SLI scaling has nothing to do with compute performance, unless you are hitting a PCIe bandwidth bottleneck or have to jump over QPI you can have 1:1 scaling with multiple GPUs.

You can switch to "grouped" above the charts to change it.

...and you could argue that it does make sense for showing how X fares across categories Y,Z,T... The height of the bar is proportional to the average performance across the different frameworks.

seriously...

Treating big companies as monoliths never works well. Different teams will inevitably choose to use different hardware, but the nVidia Tesla Kxx is popular among a lot of these "big four" types.

Likely not these GPUs unless it's for small scale projects.

You need a Tesla card for most enterprise scale compute features.