diff --git a/src/data/iterative_dmatrix.cc b/src/data/iterative_dmatrix.cc
index 19dd3490d..ebb9d1a98 100644
--- a/src/data/iterative_dmatrix.cc
+++ b/src/data/iterative_dmatrix.cc
@@ -58,6 +58,13 @@ void GetCutsFromRef(std::shared_ptr<DMatrix> ref_, bst_feature_t n_features, Bat
     }
   };
   auto ellpack = [&]() {
+    // workaround ellpack being initialized from CPU.
+    if (p.gpu_id == Context::kCpuId) {
+      p.gpu_id = ref_->Ctx()->gpu_id;
+    }
+    if (p.gpu_id == Context::kCpuId) {
+      p.gpu_id = 0;
+    }
     for (auto const& page : ref_->GetBatches<EllpackPage>(p)) {
       GetCutsFromEllpack(page, p_cuts);
       break;
@@ -172,9 +179,9 @@ void IterativeDMatrix::InitFromCPU(DataIterHandle iter_handle, float missing,
     size_t i = 0;
     while (iter.Next()) {
       if (!p_sketch) {
-        p_sketch.reset(new common::HostSketchContainer{batch_param_.max_bin,
-                                                       proxy->Info().feature_types.ConstHostSpan(),
-                                                       column_sizes, false, ctx_.Threads()});
+        p_sketch.reset(new common::HostSketchContainer{
+            batch_param_.max_bin, proxy->Info().feature_types.ConstHostSpan(), column_sizes,
+            !proxy->Info().group_ptr_.empty(), ctx_.Threads()});
       }
       HostAdapterDispatch(proxy, [&](auto const& batch) {
         proxy->Info().num_nonzero_ = batch_nnz[i];
diff --git a/tests/python-gpu/test_device_quantile_dmatrix.py b/tests/python-gpu/test_device_quantile_dmatrix.py
index 2c3a7a760..b3d7ef660 100644
--- a/tests/python-gpu/test_device_quantile_dmatrix.py
+++ b/tests/python-gpu/test_device_quantile_dmatrix.py
@@ -139,3 +139,17 @@ class TestDeviceQuantileDMatrix:
             booster.predict(xgb.DMatrix(d_m.get_data())),
             atol=1e-6,
         )
+
+    def test_ltr(self) -> None:
+        import cupy as cp
+        X, y, qid, w = tm.make_ltr(100, 3, 3, 5)
+        # make sure GPU is used to run sketching.
+        cpX = cp.array(X)
+        Xy_qdm = xgb.QuantileDMatrix(cpX, y, qid=qid, weight=w)
+        Xy = xgb.DMatrix(X, y, qid=qid, weight=w)
+        xgb.train({"tree_method": "gpu_hist", "objective": "rank:ndcg"}, Xy)
+
+        from_dm = xgb.QuantileDMatrix(X, weight=w, ref=Xy)
+        from_qdm = xgb.QuantileDMatrix(X, weight=w, ref=Xy_qdm)
+
+        assert tm.predictor_equal(from_qdm, from_dm)
diff --git a/tests/python/test_quantile_dmatrix.py b/tests/python/test_quantile_dmatrix.py
index 65ccfa4e5..9000d9988 100644
--- a/tests/python/test_quantile_dmatrix.py
+++ b/tests/python/test_quantile_dmatrix.py
@@ -9,7 +9,9 @@ from testing import (
     make_batches,
     make_batches_sparse,
     make_categorical,
+    make_ltr,
     make_sparse_regression,
+    predictor_equal,
 )
 
 import xgboost as xgb
@@ -218,6 +220,16 @@ class TestQuantileDMatrix:
         b = booster.predict(qXy)
         np.testing.assert_allclose(a, b)
 
+    def test_ltr(self) -> None:
+        X, y, qid, w = make_ltr(100, 3, 3, 5)
+        Xy_qdm = xgb.QuantileDMatrix(X, y, qid=qid, weight=w)
+        Xy = xgb.DMatrix(X, y, qid=qid, weight=w)
+        xgb.train({"tree_method": "hist", "objective": "rank:ndcg"}, Xy)
+
+        from_qdm = xgb.QuantileDMatrix(X, weight=w, ref=Xy_qdm)
+        from_dm = xgb.QuantileDMatrix(X, weight=w, ref=Xy)
+        assert predictor_equal(from_qdm, from_dm)
+
     # we don't test empty Quantile DMatrix in single node construction.
     @given(
         strategies.integers(1, 1000),
diff --git a/tests/python/testing.py b/tests/python/testing.py
index 63d33de97..f0f09911f 100644
--- a/tests/python/testing.py
+++ b/tests/python/testing.py
@@ -466,7 +466,22 @@ def make_categorical(
     return df, label
 
 
-def _cat_sampled_from():
+def make_ltr(
+    n_samples: int, n_features: int, n_query_groups: int, max_rel: int
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """Make a dataset for testing LTR."""
+    rng = np.random.default_rng(1994)
+    X = rng.normal(0, 1.0, size=n_samples * n_features).reshape(n_samples, n_features)
+    y = rng.integers(0, max_rel, size=n_samples)
+    qid = rng.integers(0, n_query_groups, size=n_samples)
+    w = rng.normal(0, 1.0, size=n_query_groups)
+    w -= np.min(w)
+    w /= np.max(w)
+    qid = np.sort(qid)
+    return X, y, qid, w
+
+
+def _cat_sampled_from() -> strategies.SearchStrategy:
     @strategies.composite
     def _make_cat(draw):
         n_samples = draw(strategies.integers(2, 512))
@@ -775,6 +790,19 @@ class DirectoryExcursion:
                 os.remove(f)
 
 
+def predictor_equal(lhs: xgb.DMatrix, rhs: xgb.DMatrix) -> bool:
+    """Assert whether two DMatrices contain the same predictors."""
+    lcsr = lhs.get_data()
+    rcsr = rhs.get_data()
+    return all(
+        (
+            np.array_equal(lcsr.data, rcsr.data),
+            np.array_equal(lcsr.indices, rcsr.indices),
+            np.array_equal(lcsr.indptr, rcsr.indptr),
+        )
+    )
+
+
 @contextmanager
 def captured_output():
     """Reassign stdout temporarily in order to test printed statements