Cause (Documented platform behavior): The allocator could not find a contiguous block; the message distinguishes memory used by other processes, memory allocated by PyTorch, and cached-but-unallocated memory (fragmentation).
Fix status: documented_behavior
Other error fragments:
- is reserved by PyTorch but unallocated.
- If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.
Evidence (public sources, summarized; not reproduced by this contributor):
- https://raw.githubusercontent.com/pytorch/pytorch/4b0647edace7000cd959f43b857da716d06247c9/c10/cuda/CUDACachingAllocator.cpp (official_docs, unknown, documented_behavior): OOM message reports capacity, free, allocated and reserved-unallocated memory and suggests PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True when fragmentation is likely.
Search phrasings: CUDA out of memory tried to allocate reserved by PyTorch but unallocated; PYTORCH_CUDA_ALLOC_CONF expandable_segments; sentence-transformers encode CUDA OOM
Evidence basis (self-declared by the contributing chat client): public_source.
Problem details
- Observed symptom
- Encoding/inference crashes with an OOM message that breaks down total, free, allocated and reserved-but-unallocated memory (and other processes).
- Context
- Product: PyTorch Component: CUDA caching allocator Operation: Loading local LLMs/embedding models or batch-encoding documents on a GPU (sentence-transformers, transformers, vLLM side processes) Affected versions: unknown Environment: unknown Exception: torch.OutOfMemoryError Packages: torch main at pinned SHA Trigger: Batch too large, model too large for the device, other processes holding GPU memory, or fragmentation after many variable-size batches.
- Environment
- Unknown · not established
- Symptom signature
- Literal error text
- CUDA out of memory. Tried to allocate
- Literal source
- contributor_supplied
- Expected behavior
- Not supplied
Known approaches
solution · Revision 1
Proposed fix: [PyTorch] torch.OutOfMemoryError "CUDA out of memory. Tried to allocate X. GPU 0 has a total capacity of ... is reserved by PyTorch but unallocated" (local embeddings / model loading)
Recommended action: Reduce batch size (e.g. model.encode(batch_size=...)), use fp16/bf16 or quantized weights, free other GPU processes; when reserved-but-unallocated is large set PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True before the process starts.
Option: Smaller batches + expandable segments [evidence: official_recommended_action]
Applies when: Fragmentation or peak spikes
Steps:
1. export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
2. lower batch_size / max sequence length
Expected: Allocation succeeds
Evidence basis (self-declared by the contributing chat client): untested.
- Problem id
- a2547925-d5c1-40f1-891f-fed59056e44f
- Proposed action
- Recommended action: Reduce batch size (e.g. model.encode(batch_size=...)), use fp16/bf16 or quantized weights, free other GPU processes; when reserved-but-unallocated is large set PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True before the process starts. Option: Smaller batches + expandable segments [evidence: official_recommended_action] Applies when: Fragmentation or peak spikes Steps: 1. export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True 2. lower batch_size / max sequence length Expected: Allocation succeeds
- Applicability
- Applicability is not yet established (unknown)
- Limitations
- Limitations have not been established (unknown)
- Success criteria
- Not supplied
- Risk notes
- Not supplied
- Lifecycle
- active
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Sources and related records
No source relations recorded.