By the way, these Tesla P40 are super old. From 2016 or something. It's the common trick to buy them used to get a lot of VRAM for cheap. But I think you should really know what you're doing before investing several hundred bucks into something like this, as it comes with consequences.
this post was submitted on 11 Feb 2025
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Hey there! ๐ Sorry to bother you, but Iโm considering upgrading my homelab with some P40 or M100 GPUs. I mainly need more VRAM for my projects, and I was really inspired by the channel "Homelab AI" and their video on building a DIY 4x Nvidia P40 homeserver with 96GB of VRAM! If you havenโt seen it yet, you can check it out here: DIY 4x Nvidia P40 Homeserver for A https://youtu.be/dHTvpUlWFbk . Iโd love to hear your thoughts or any tips you might have? Thanks a lot! ๐ #ai #machinelearning #artificialintelligence #Homelab #AI #Nvidia #GPUs #P40 #M100 #VRAM #DIY #TechProjects #MachineLearning #DataScience #ServerBuild #TechInspiration #HomeServer #CloudComputing
Yeah, that just depends on what you're trying to achieve. Depending on what kind of AI workload you have, you can scale it across 4 GPUs. Or it'll become super slow if it needs to transfer a lot of data between these GPUs. And depending on what kinds of maths is involved, a Pascal generation GPU might be perfectly fine, or it'll lack support for some of the operations involved. So yes, of course you can build that rig. Whether it's going to be useful in your scenario is a different question. But I'd argue, if you need 96GB of VRAM for more than just the sake of it, you should be able to tell... I've seen people discuss these rigs with several P40 or similar, on Reddit and in some forums and Github discussions of the software involved. You might just have to do some research and find out if your AI inference framework and the model does well on specific hardware.