Investigation into Nvidia GPU workloads reveals that Tensor cores are being hammered, just incredibly briefly.
can run on a much wider range of GPUs, including those of competitors . What this all comes down to, then, is the question of whether DLSS scaling does indeed lean heavily on those AI-accelerating Tensor cores, as Nvidia claims.
Well, now we seemingly have an answer, of sorts. And it turns out DLSS really does need those Tensor cores.to drill down into the workloads running on various parts of an Nvidia RTX 4090 GPU.and measured Tensor core utilisation. Looking at average Tensor core usage, the figures under DLSS were extremely low, less than 1%.
Initial investigations suggested even the peak utilisation registered in the 4-9% range, implying that while the Tensor cores were being used, they probably weren't actually essential. However, increasing the polling rate revealed that peak utilisation is in fact in excess of 90%, but only for brief periods measured in microseconds.
When you think about it, that makes sense. The upscaling process has to be ultra quick if it's not to slow down the overall frame rate. It has to take a rendered frame, process it, do whatever calculations are required for the upscaling, and output the full upscaled frame before the 3D pipeline has had time to generate a new frame.
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