diff --git a/sql-plugin/src/main/scala/com/nvidia/spark/rapids/BatchWithPartitionData.scala b/sql-plugin/src/main/scala/com/nvidia/spark/rapids/BatchWithPartitionData.scala
index 02e5ee118db..6d640683c07 100644
--- a/sql-plugin/src/main/scala/com/nvidia/spark/rapids/BatchWithPartitionData.scala
+++ b/sql-plugin/src/main/scala/com/nvidia/spark/rapids/BatchWithPartitionData.scala
@@ -340,6 +340,8 @@ object BatchWithPartitionDataUtils {
    * Splits the input ColumnarBatch into smaller batches, wraps these batches with partition
    * data, and returns them as a sequence of [[BatchWithPartitionData]].
    *
+   * This function does not take ownership of `batch`, and callers should make sure to close.
+   *
    * @note Partition values are merged with the columnar batches lazily by the resulting Iterator
    *       to save GPU memory.
    * @param batch                     Input ColumnarBatch.
@@ -502,9 +504,10 @@ object BatchWithPartitionDataUtils {
           throw new SplitAndRetryOOM("GPU OutOfMemory: cannot split input with one row")
         }
         // Split the batch into two halves
-        val cb = batchWithPartData.inputBatch.getColumnarBatch()
-        splitAndCombineBatchWithPartitionData(cb, splitPartitionData,
-          batchWithPartData.partitionSchema)
+        withResource(batchWithPartData.inputBatch.getColumnarBatch()) { cb =>
+          splitAndCombineBatchWithPartitionData(cb, splitPartitionData,
+            batchWithPartData.partitionSchema)
+        }
       }
     }
   }
diff --git a/tests/src/test/scala/com/nvidia/spark/rapids/BatchWithPartitionDataSuite.scala b/tests/src/test/scala/com/nvidia/spark/rapids/BatchWithPartitionDataSuite.scala
index ac4b5d89b47..6c9f59e8ece 100644
--- a/tests/src/test/scala/com/nvidia/spark/rapids/BatchWithPartitionDataSuite.scala
+++ b/tests/src/test/scala/com/nvidia/spark/rapids/BatchWithPartitionDataSuite.scala
@@ -78,12 +78,17 @@ class BatchWithPartitionDataSuite extends RmmSparkRetrySuiteBase with SparkQuery
       withResource(buildBatch(getSampleValueData)) { valueBatch =>
         withResource(buildBatch(partCols)) { partBatch =>
           withResource(GpuColumnVector.combineColumns(valueBatch, partBatch)) { expectedBatch =>
+            // we incRefCounts here because `addPartitionValuesToBatch` takes ownership of
+            // `valueBatch`, but we are keeping it alive since its columns are part of
+            // `expectedBatch`
+            GpuColumnVector.incRefCounts(valueBatch)
             val resultBatchIter = BatchWithPartitionDataUtils.addPartitionValuesToBatch(valueBatch,
               partRows, partValues, partSchema, maxGpuColumnSizeBytes)
             withResource(resultBatchIter) { _ =>
               RmmSpark.forceSplitAndRetryOOM(RmmSpark.getCurrentThreadId)
               // Assert that the final count of rows matches expected batch
-              val rowCounts = resultBatchIter.map(_.numRows()).sum
+              // We also need to close each batch coming from `resultBatchIter`.
+              val rowCounts = resultBatchIter.map(withResource(_){_.numRows()}).sum
               assert(rowCounts == expectedBatch.numRows())
             }
           }