diff --git a/rust/lance/src/index/vector/utils.rs b/rust/lance/src/index/vector/utils.rs
index 8b1a000fb1b..3358f9093d5 100644
--- a/rust/lance/src/index/vector/utils.rs
+++ b/rust/lance/src/index/vector/utils.rs
@@ -3,12 +3,12 @@
 
 use std::sync::Arc;
 
-use arrow_array::{cast::AsArray, ArrayRef, FixedSizeListArray, RecordBatch};
+use arrow_array::{cast::AsArray, Array, ArrayRef, FixedSizeListArray, RecordBatch};
 use futures::StreamExt;
 use lance_arrow::{interleave_batches, DataTypeExt};
 use lance_core::datatypes::Schema;
 use lance_linalg::distance::DistanceType;
-use log::info;
+use log::{info, warn};
 use rand::rngs::SmallRng;
 use rand::seq::{IteratorRandom, SliceRandom};
 use rand::SeedableRng;
@@ -84,6 +84,58 @@ fn get_column_from_batch(batch: &RecordBatch, column: &str) -> Result<ArrayRef>
     Ok(current_array)
 }
 
+async fn estimate_multivector_vectors_per_row(
+    dataset: &Dataset,
+    column: &str,
+    num_rows: usize,
+) -> Result<usize> {
+    if num_rows == 0 {
+        return Ok(1030);
+    }
+
+    let projection = dataset.schema().project(&[column])?;
+
+    // Try a few random samples first (fast path).
+    let sample_batch_size = std::cmp::min(64, num_rows);
+    for _ in 0..8 {
+        let batch = dataset.sample(sample_batch_size, &projection).await?;
+        let array = get_column_from_batch(&batch, column)?;
+        let list_array = array.as_list::<i32>();
+        for i in 0..list_array.len() {
+            if list_array.is_null(i) {
+                continue;
+            }
+            let len = list_array.value_length(i) as usize;
+            if len > 0 {
+                return Ok(len);
+            }
+        }
+    }
+
+    // Fallback: scan a small prefix to find a non-null example. This avoids rare
+    // flakiness when values are extremely sparse.
+    let mut scanner = dataset.scan();
+    scanner.project(&[column])?;
+    let column_expr = lance_datafusion::logical_expr::field_path_to_expr(column)?;
+    scanner.filter_expr(column_expr.is_not_null());
+    scanner.limit(Some(std::cmp::min(num_rows, 1024) as i64), None)?;
+    let batch = scanner.try_into_batch().await?;
+    let array = get_column_from_batch(&batch, column)?;
+    let list_array = array.as_list::<i32>();
+    for i in 0..list_array.len() {
+        let len = list_array.value_length(i) as usize;
+        if len > 0 {
+            return Ok(len);
+        }
+    }
+
+    warn!(
+        "Could not find a non-empty multivector value for column {}, falling back to n=1030",
+        column
+    );
+    Ok(1030)
+}
+
 /// Get the vector dimension of the given column in the schema.
 pub fn get_vector_dim(schema: &Schema, column: &str) -> Result<usize> {
     let field = schema.field(column).ok_or(Error::Index {
@@ -231,11 +283,12 @@ pub async fn maybe_sample_training_data(
         arrow::datatypes::DataType::List(_) => {
             // for multivector, we need `sample_size_hint` vectors for training,
             // but each multivector is a list of vectors, but we don't know how many
-            // vectors are in each multivector. For now we just assume there are 1030 vectors
-            // in each multivector (Copali case).
+            // vectors are in each multivector. Estimate this by looking at a non-null row.
             // Set a minimum sample size of 128 to avoid too small samples,
             // it's not a problem because 128 multivectors is just about 64 MiB
-            sample_size_hint.div_ceil(1030).max(128)
+            let vectors_per_row =
+                estimate_multivector_vectors_per_row(dataset, column, num_rows).await?;
+            sample_size_hint.div_ceil(vectors_per_row).max(128)
         }
         _ => sample_size_hint,
     };
@@ -439,6 +492,11 @@ fn random_ranges(
 mod tests {
     use super::*;
 
+    use arrow_array::types::Float32Type;
+    use lance_datagen::{array, gen_batch, ArrayGeneratorExt, Dimension, RowCount};
+
+    use crate::dataset::InsertBuilder;
+
     #[rstest::rstest]
     #[test]
     fn test_random_ranges(
@@ -461,4 +519,60 @@ mod tests {
         });
         assert_eq!(ranges, expected.collect::<Vec<_>>());
     }
+
+    #[tokio::test]
+    async fn test_maybe_sample_training_data_multivector_infers_vectors_per_row() {
+        let nrows: usize = 2000;
+        let dims: u32 = 8;
+        let vectors_per_row: u32 = 2;
+
+        let mv = array::cycle_vec_var(
+            array::rand_vec::<Float32Type>(Dimension::from(dims)),
+            Dimension::from(vectors_per_row),
+            Dimension::from(vectors_per_row + 1),
+        );
+
+        let data = gen_batch()
+            .col("mv", mv)
+            .into_batch_rows(RowCount::from(nrows as u64))
+            .unwrap();
+
+        let dataset = InsertBuilder::new("memory://")
+            .execute(vec![data])
+            .await
+            .unwrap();
+
+        let training_data = maybe_sample_training_data(&dataset, "mv", 1000)
+            .await
+            .unwrap();
+        assert_eq!(training_data.len(), 1000);
+    }
+
+    #[tokio::test]
+    async fn test_estimate_multivector_vectors_per_row_fallback_1030() {
+        let nrows: usize = 256;
+        let dims: u32 = 8;
+
+        let mv = array::cycle_vec_var(
+            array::rand_vec::<Float32Type>(Dimension::from(dims)),
+            Dimension::from(2),
+            Dimension::from(3),
+        )
+        .with_random_nulls(1.0);
+
+        let data = gen_batch()
+            .col("mv", mv)
+            .into_batch_rows(RowCount::from(nrows as u64))
+            .unwrap();
+
+        let dataset = InsertBuilder::new("memory://")
+            .execute(vec![data])
+            .await
+            .unwrap();
+
+        let n = estimate_multivector_vectors_per_row(&dataset, "mv", nrows)
+            .await
+            .unwrap();
+        assert_eq!(n, 1030);
+    }
 }