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nemo-mbridge-perf-memory-tuning

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Techniques for reducing peak GPU memory in Megatron Bridge — expandable segments, PEFT + SP input re-gather, parallelism resizing, activation recompute, CPU offloading constraints, and common OOM fixes.

skills/nemo-mbridge-perf-memory-tuning/SKILL.md

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# Memory Tuning

Stable docs: @docs/parallelisms.md
Card: @skills/nemo-mbridge-perf-memory-tuning/card.yaml

## What It Is

GPU OOM failures during training often stem from memory **fragmentation** rather
than raw capacity.  PyTorch's default CUDA allocator can leave unusable gaps
between allocations.  The single most effective fix is:

```bash
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
```
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