diff --git a/scanpy/neighbors/__init__.py b/scanpy/neighbors/__init__.py
index c2977682e8..e0395dfeaa 100644
--- a/scanpy/neighbors/__init__.py
+++ b/scanpy/neighbors/__init__.py
@@ -315,7 +315,9 @@ def compute_neighbors_umap(
     return knn_indices, knn_dists, forest
 
 
-def compute_neighbors_rapids(X: np.ndarray, n_neighbors: int):
+def compute_neighbors_rapids(
+    X: np.ndarray, n_neighbors: int, metric: _Metric = 'euclidean'
+):
     """Compute nearest neighbors using RAPIDS cuml.
 
     Parameters
@@ -324,6 +326,9 @@ def compute_neighbors_rapids(X: np.ndarray, n_neighbors: int):
         The data to compute nearest neighbors for.
     n_neighbors
         The number of neighbors to use.
+    metric
+        The metric to use to compute distances in high dimensional space.
+        This string must match a valid predefined metric in RAPIDS cuml.
 
         Returns
     -------
@@ -331,7 +336,7 @@ def compute_neighbors_rapids(X: np.ndarray, n_neighbors: int):
     """
     from cuml.neighbors import NearestNeighbors
 
-    nn = NearestNeighbors(n_neighbors=n_neighbors)
+    nn = NearestNeighbors(n_neighbors=n_neighbors, metric=metric)
     X_contiguous = np.ascontiguousarray(X, dtype=np.float32)
     nn.fit(X_contiguous)
     knn_distsq, knn_indices = nn.kneighbors(X_contiguous)
@@ -755,10 +760,6 @@ def compute_neighbors(
             logg.warning(f'n_obs too small: adjusting to `n_neighbors = {n_neighbors}`')
         if method == 'umap' and not knn:
             raise ValueError('`method = \'umap\' only with `knn = True`.')
-        if method == 'rapids' and metric != 'euclidean':
-            raise ValueError(
-                "`method` 'rapids' only supports the 'euclidean' `metric`."
-            )
         if method not in {'umap', 'gauss', 'rapids'}:
             raise ValueError("`method` needs to be 'umap', 'gauss', or 'rapids'.")
         if self._adata.shape[0] >= 10000 and not knn:
@@ -782,7 +783,9 @@ def compute_neighbors(
             else:
                 self._distances = _distances
         elif method == 'rapids':
-            knn_indices, knn_distances = compute_neighbors_rapids(X, n_neighbors)
+            knn_indices, knn_distances = compute_neighbors_rapids(
+                X, n_neighbors, metric=metric
+            )
         else:
             # non-euclidean case and approx nearest neighbors
             if X.shape[0] < 4096: