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This is interesting, but I'm more interested in self-hosting. I already have a lot of GPUs (some running some not.) Does this have a self-hosting option so I can use the GPUs I already have?
Earlier quoted context omitted.
We haven't tested with MIG or vGPU, but I think it would work since it's essentially physically partitioning the GPU. One of our main goals for the near future is to allow GPU sharing. This would be better than MIG or vGPU since we'd allow users to use the entire GPU memory instead of restricting them to a fraction.
We had a hell of a time dealing with the licensing issues and ultimately just gave up and give people whole GPUs. What are you doing to reset the GPU to clean state after a run? It's surprisingly complicated to do this securely (we're writing up a back-to-back sequence of audits we did with Atredis and Tetrel; should be publishing in a month or two).
Would be possible to mix it with Blender?
Down the line, we could see this being used for batched render jobs (i.e. to replace a render farm).
This could be perfect for us. We need very limited bandwidth but have high compute needs.
Ah this is quite interesting! I had a usecase where I needed a GPU-over-IP but only for transcoding videos. I had a not-so-powerful AMD GPU in my homelab server that somehow kept crashing the kernel any time I tried to encode videos with it and also an NVIDIA RTX 3080 in a gaming machine. So I wrote https://github.com/steelbrain/ffmpeg-over-ip and had the server running in the windows machine and the client in the me…