# RuntimeError: CUBLAS_STATUS_INVALID_VALUE when calling cublasGemmStridedBatchedEx with batch_count > 0 but A/B/C matrices have incompatible dimensions

- **ID:** `cuda/cublas-gemm-batched-wrong-rank`
- **Domain:** cuda
- **Category:** runtime_error
- **Error Code:** `CUBLAS_STATUS_INVALID_VALUE`
- **Verification:** ai_generated
- **Fix Rate:** 80%

## Root Cause

cuBLAS batched GEMM requires that the leading dimensions (lda, ldb, ldc) and strides of matrices A, B, and C are consistent with the matrix dimensions and batch count; mismatched sizes cause an invalid value error.

## Version Compatibility

| Version | Status | Introduced | Deprecated |
|---------|--------|------------|------------|
| CUDA 11.7 | active | — | — |
| CUDA 12.0 | active | — | — |
| cuBLAS 11.10 | active | — | — |
| cuBLAS 12.0 | active | — | — |

## Workarounds

1. **Verify that lda >= m, ldb >= k, ldc >= m, and that strideA >= m*k, strideB >= k*n, strideC >= m*n for each batch. Adjust matrix allocation accordingly.** (85% success)
   ```
   Verify that lda >= m, ldb >= k, ldc >= m, and that strideA >= m*k, strideB >= k*n, strideC >= m*n for each batch. Adjust matrix allocation accordingly.
   ```
2. **Use PyTorch's `torch.bmm` or `torch.matmul` with batched tensors instead of raw cuBLAS calls, as these handle dimension validation internally.** (90% success)
   ```
   Use PyTorch's `torch.bmm` or `torch.matmul` with batched tensors instead of raw cuBLAS calls, as these handle dimension validation internally.
   ```

## Dead Ends

- **** — Transposition changes the memory layout and may cause silent data corruption; the correct fix is to compute proper strides and leading dimensions. (90% fail)
- **** — This bypasses the error but loses the performance benefit of batching; the underlying dimension issue remains for actual batched use. (70% fail)