I never had one fail, but I had to go through some big hurdles to use them at work (University research lab) properly.

Namely, there are 2 big issues: - Virtualization: Getting vGPU functionality out of them (most of the time, there's no point in having a full 1080 Ti for only 1 job, we want to have it split on more than 1 VM). - Kubernetes workloads: I'm running a simple workload that uses the GPU (for testing for instance) and k8s wants it all for only one pod.