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CUDA Caching Allocator Issue

CUDA caching allocator issues cause memory not being released back to the GPU when expected.

Quick answer

CUDA caching allocator issues cause memory not being released back to the GPU when expected.

Memory#cuda#caching-allocator#memory#pytorch#oom#release

What this failure is

CUDA Caching Allocator Issue is a Memory failure seen during ML training runs. CUDA caching allocator issues cause memory not being released back to the GPU when expected. Common tags: Cuda, Caching Allocator, Memory, Pytorch.

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Why it happens (the mechanism)

Caching allocator keeps memory blocks for reuse. Blocks are not released back to GPU driver. Reserved memory grows over time. Memory fragmentation prevents release. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.

What you'll observe

  • GPU memory not released after deleting tensors
  • Memory usage grows over training
  • OOM despite torch.cuda.empty_cache() not helping

Common symptoms and what they mean

SymptomWhy it happens
nvidia-smi shows memory not releasedCaching allocator keeps memory blocks for reuse
torch.cuda.memory_allocated() vs nvidia-smi shows different valuesBlocks are not released back to GPU driver
Caching allocator holds onto memory blocksReserved memory grows over time

Which systems are affected

  • Long-running training with varying tensor sizes
  • Models with dynamic memory patterns
  • Training with multiple models on same GPU

How to confirm this is the problem

Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.

  • ✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
  • ✓Verified signal present: nvidia-smi shows memory not released
  • ✓Verified signal present: torch.cuda.memory_allocated() vs nvidia-smi shows different values
  • ✓Verified signal present: Caching allocator holds onto memory blocks
  • ✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.

The fix and the prevention pattern

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CUDA errors in context

CUDA reports errors asynchronously, so the traceback usually points at whatever line synchronised next rather than the one at fault. The hub covers every common CUDA error and how to make it report honestly.

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Related failures to investigate next

Cpu Offloading Overhead often co-occurs with this failure because both share underlying assumptions about memory; if you fix this entry but still see drift, validate the related primary cause next. cpu offloading overhead →
Gradient Checkpointing Tradeoff often co-occurs with this failure because both share underlying assumptions about memory; if you fix this entry but still see drift, validate the related neighboring cause next. gradient checkpointing tradeoff →
Cuda Oom often co-occurs with this failure because both share underlying assumptions about memory; if you fix this entry but still see drift, validate the related neighboring cause next. cuda oom →

Root cause

  • Caching allocator keeps memory blocks for reuse
  • Blocks are not released back to GPU driver
  • Reserved memory grows over time
  • Memory fragmentation prevents release

The fix and how to prevent it

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References

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Related Memory errors

Activation Memory Spike

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Memory Summary Tool Usage

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PyTorch Caching Allocator Debug

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CPU RAM Exhaustion During Training

Memory · high