diff --git a/sklearn/metrics/_pairwise_distances_reduction/_base.pyx.tp b/sklearn/metrics/_pairwise_distances_reduction/_base.pyx.tp
index 6a4d879667d3a..bab9952e22e2d 100644
--- a/sklearn/metrics/_pairwise_distances_reduction/_base.pyx.tp
+++ b/sklearn/metrics/_pairwise_distances_reduction/_base.pyx.tp
@@ -32,6 +32,7 @@ from sklearn import get_config
 from sklearn.utils import check_scalar
 from ...utils._openmp_helpers import _openmp_effective_n_threads
 from ...utils._typedefs import DTYPE, SPARSE_INDEX_TYPE
+from ...utils.sparsefuncs_fast import _sqeuclidean_row_norms_sparse
 
 cnp.import_array()
 
@@ -103,23 +104,6 @@ cdef DTYPE_t[::1] _sqeuclidean_row_norms32_dense(
     return squared_row_norms
 
 
-cdef DTYPE_t[::1] _sqeuclidean_row_norms64_sparse(
-    const DTYPE_t[:] X_data,
-    const SPARSE_INDEX_TYPE_t[:] X_indptr,
-    ITYPE_t num_threads,
-):
-    cdef:
-        ITYPE_t n = X_indptr.shape[0] - 1
-        SPARSE_INDEX_TYPE_t X_i_ptr, idx = 0
-        DTYPE_t[::1] squared_row_norms = np.zeros(n, dtype=DTYPE)
-
-    for idx in prange(n, schedule='static', nogil=True, num_threads=num_threads):
-        for X_i_ptr in range(X_indptr[idx], X_indptr[idx+1]):
-            squared_row_norms[idx] += X_data[X_i_ptr] * X_data[X_i_ptr]
-
-    return squared_row_norms
-
-
 {{for name_suffix, INPUT_DTYPE_t, INPUT_DTYPE in implementation_specific_values}}
 
 from ._datasets_pair cimport DatasetsPair{{name_suffix}}
@@ -131,10 +115,10 @@ cpdef DTYPE_t[::1] _sqeuclidean_row_norms{{name_suffix}}(
 ):
     if issparse(X):
         # TODO: remove this instruction which is a cast in the float32 case
-        # by moving squared row norms computations in MiddleTermComputer. 
+        # by moving squared row norms computations in MiddleTermComputer.
         X_data = np.asarray(X.data, dtype=DTYPE)
         X_indptr = np.asarray(X.indptr, dtype=SPARSE_INDEX_TYPE)
-        return _sqeuclidean_row_norms64_sparse(X_data, X_indptr, num_threads)
+        return _sqeuclidean_row_norms_sparse(X_data, X_indptr, num_threads)
     else:
         return _sqeuclidean_row_norms{{name_suffix}}_dense(X, num_threads)