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How to Fix CUDA Out of Memory
Error signature: torch.cuda.OutOfMemoryError
Last reviewed: 2026-07-27
What the error means
This error indicates a failure in the GPU allocator. The exact cause depends on the first failing operation and environment; do not treat later asynchronous errors as the root cause.
Common symptoms
- Allocation fails while loading or generating
- Worker exits as VRAM approaches capacity
Likely explanation
- Model, KV cache, or batch does not fit available VRAM
- Fragmented reserved memory leaves no contiguous allocation
Suggested diagnostic steps
- Capture the complete error and the first failing operation.
- Record framework, package, driver, GPU, and operating-system versions.
- Reproduce with the smallest workload and inspect memory and health output.
Step-by-step fixes
- Correct the first incompatible resource, version, or configuration identified by the checks.
- Restart from a clean process and rerun the minimal reproduction.
- Scale the workload down or choose compatible capacity before restoring concurrency.
Known workaround
- Reduce batch size, concurrency, or context while the root cause is being corrected.
Current resolution status
Tracked; resolution depends on environment and version.
Confirmed source issues
No individual issue has been approved as evidence yet. This candidate guide remains noindex.
Discovery searches
- vllm-project/vllm — Search for reports matching torch.cuda.OutOfMemoryError in GPU allocator.
- pytorch/pytorch — Search for reports matching torch.cuda.OutOfMemoryError in GPU allocator.
- comfyanonymous/ComfyUI — Search for reports matching torch.cuda.OutOfMemoryError in GPU allocator.