I believe the `MXNET_GPU_MEM_POOL_RESERVE` environment variable is just a hint to `MXNet` to release all ever allocated but now **FREED** gpu memories, which is served as possible reuse in the future. If your network is large (for example, the gpu cannot run two such networks simultaneously), one way to handle this is to implement the `memory sharing` between shallow layers and deeper layers' calculation, or more aggressive `inplace` operators, either of these two methods could involve some workload to the DL frameworks.
BTW, have you tried the network using `TF` with same GPU memory budget without an `OOM` error raised ? As far as I know, the MXNet already have a good implementation to use less gpu memory. And, the `GPU Load` means the calculation ability (for example, the cuda cores) used by current application, but not memory used by 81 % in my opinion, where higher means better use of GPU. --- [Visit Topic](https://discuss.mxnet.io/t/how-to-limit-gpu-memory-usage/6304/4) or reply to this email to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.mxnet.io/email/unsubscribe/bbb47aa1f63b1848f22361e42d3ef3c9e0e33f5cd6123711b19c9b4d0cb3bb11).
