[D] GPU requirements for implicit neural representations & neural radiance fields

10 points · submitted Mar 2022 by u/Ungreon · Discussion

Just wanted to ask anyone with experience working in the field of implicit neural represenations regarding the compute requirements you've experienced when developing models. Mainly looking in the domain of neural radiance fields (https://www.matthewtancik.com/nerf). I do have cluster access for evaluating projects that are more mature in the development pipeline, but wanted to gauge if anyone had any advice regarding what has worked when still in earlier development mainly when working on my standalone PC.

Thanks so much for any help!

[D] GPU requirements for implicit neural representations & neural radiance fields

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morsingher 4 points · Mar 2022

Hi, I have tested many NeRF-like approaches and they are generally quite lightweight in terms of memory. Two main reasons:

The model is a simple MLP which requires a few MB of memory.
You can control as parameters both the number of rays processed per iteration and the number of points (per ray) processed in parallel by the network.

I use small consumer GPUs like Titan Xp or GTX 1080 Ti and I never had OOM problems. Of course, if you have a big cluster, the model will train much faster.

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