Yesterday, AMD showed off the first real-time benchmarks of the RadeonVega graphics card against the NVIDIA Pascal based Tesla P100 in deep learning benchmarks. In its first attempt, the RTG developed GPU was able to give NVIDIA's best compute card from last year a good beating but there's more to the benchmarks.

AMD Radeon Vega Vs NVIDIA Pascal Tesla P100 Deep Learning Performance Detailed

NVIDIA launched the Tesla P100 based on Pascal GP100 back in early 2016. Since then, it has been the fastest compute solution available to date. NVIDIA kicked off 2017 with the announcement of the next chapter in graphics deep learning. They announced the Tesla V100 based on Volta GV100 at GTC 2017. We already know the specs of these high-performance compute cards.

The Tesla P100 is a cut down configuration and features 3584 Cores for 10.6 TFLOPs (FP32) and 21.2 TFLOPs (FP16). Moving on, the Radeon Vega Frontier Editionwill have 4096 Cores for 13.0 TFLOPs (FP32) and 25 TFLOPs (FP 64). NVIDIA's Tesla V100 is also a cut down configuration like the Tesla P100 but has vast number of cores. The chip houses 5120 cores while there are in fact 5376 cores on the GPU.

The chip delivers an astonishing amount of compute rated at 15 TFLOPs (FP32) and 120 Tensor TFLOPs (FP16) with the new Tensor Cores. The Tensor cores are dedicated units inside the Volta chip which are used for deeplearning training and deliver up to 6 times higher FP16 output than GP100 or any GPU of its caliber.