CUDA Shared Memory Limit Exceeded
CUDA shared memory limits are reached when kernels use too much shared memory per block.
CUDA shared memory limits are reached when kernels use too much shared memory per block.
What this failure is
CUDA Shared Memory Limit Exceeded is a Memory failure seen during ML training runs. CUDA shared memory limits are reached when kernels use too much shared memory per block. Common tags: Cuda, Shared Memory, Kernel, Memory.
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Why it happens (the mechanism)
Shared memory request exceeds per-block limit. Kernel compiled with too many threads per block. Reduction operations with large input size. 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
- CUDA kernel fails to launch with shared memory error
- Reduce shared memory usage to fit within limits
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| RuntimeError: too much shared memory requested | Shared memory request exceeds per-block limit |
| cudaErrorInvalidValue: invalid kernel argument | Kernel compiled with too many threads per block |
Which systems are affected
- Custom CUDA kernels with large shared memory
- Triton kernels with large block sizes
- Tensor cores with large tile sizes
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: RuntimeError: too much shared memory requested
- ✓Verified signal present: cudaErrorInvalidValue: invalid kernel argument
- ✓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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Diagnose this failure in VS Code
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Install the free VS Code extensionCUDA 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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Root cause
- Shared memory request exceeds per-block limit
- Kernel compiled with too many threads per block
- Reduction operations with large input size
The fix and how to prevent it
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