Nvidia's DLSS upscaling really does need those AI-accelerating Tensor cores after all

pcgamer.com:

Nvidia has form when it comes to locking out older GPUs from new technologies and features. So when it wheeled out DLSS upscaling technology, limiting it to the latest GPU architecture at the time, there was always the question of whether that was strictly necessary. If, perhaps, DLSS could run on older GPUs, was it just that Nvidia preferred to help the generational upsell by restricting the technology to newer hardware.

That suspicion is only heightened by the fact that both AMD's FSR upscaling and Intel's XeSS can run on a much wider range of GPUs, including those of competitors (for absolute clarity, Intel's XeSS came in two flavours, one widely compatible, the other requiring Intel Arc GPUs). 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.

An intrepid Reddit poster, going under the handle Bluedot55, leveraged Nvidia's Nsight Systems GPU metric tools to drill down into the workloads running on various parts of an Nvidia RTX 4090 GPU.

Bluedot55 ran both DLSS and third party scalers on an Nvidia RTX 4090 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.