Tensor Parallel Memory
Tensor parallel training splits model across GPUs, but communication buffers and synchronization can still cause OOM.
Tensor parallel training splits model across GPUs, but communication buffers and synchronization can still cause OOM.
What this failure is
Tensor Parallel Memory is a Memory failure seen during ML training runs. Tensor parallel training splits model across GPUs, but communication buffers and synchronization can still cause OOM. Common tags: Tensor Parallel, Megatron, Model Parallel, Memory.
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Why it happens (the mechanism)
Embedding layer not split: rank 0 has all embeddings. Cross-entropy loss not properly parallelized. Attention gather operations are memory-heavy. Some operations require full tensor materialization. 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
- OOM with tensor parallelism
- Tensor parallel training is slow
- Memory imbalance across GPUs
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Memory not evenly distributed across GPUs | Embedding layer not split: rank 0 has all embeddings |
| Forward pass OOMs at gather/scatter | Cross-entropy loss not properly parallelized |
| GPU 0 has more memory than others | Attention gather operations are memory-heavy |
Which systems are affected
- Tensor parallel LLM training
- Megatron-LM style training
- Large model training across GPUs
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: Memory not evenly distributed across GPUs
- ✓Verified signal present: Forward pass OOMs at gather/scatter
- ✓Verified signal present: GPU 0 has more memory than others
- ✓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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Root cause
- Embedding layer not split: rank 0 has all embeddings
- Cross-entropy loss not properly parallelized
- Attention gather operations are memory-heavy
- Some operations require full tensor materialization
The fix and how to prevent it
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