Is there a memory mechanism in PyTorch similar to zram?

1

In operating systems, zram is commonly used to compress memory in order to reduce memory usage. I’m wondering if PyTorch has a similar mechanism for optimizing memory consumption.

2

PyTorch doesn’t offer a zram-style memory compression mechanism. Instead, it optimizes GPU RAM by using a CUDA caching allocator:

  • Memory pooling: it caches freed GPU memory blocks for reuse—avoiding frequent cudaMalloc/cudaFree calls and fragmentation, which speeds things up (github.com).
  • Use custom allocators or memory pools: advanced users can plug in their own allocators (CUDAPluggableAllocator) or use torch.cuda.MemPool alongside caching (github.com).

So, rather than zram compression of RAM, PyTorch focuses on efficient reuse and tenancy of GPU memory blocks.

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3

No — PyTorch does not have a built in memory compression like zram but you can manage memory with features like gradient checkpointing or mixed precision.

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