diff --git a/tests/tests_pytorch/strategies/test_sharded_strategy.py b/tests/tests_pytorch/strategies/test_sharded_strategy.py
index 5950178869..1a5c6d68d9 100644
--- a/tests/tests_pytorch/strategies/test_sharded_strategy.py
+++ b/tests/tests_pytorch/strategies/test_sharded_strategy.py
@@ -1,4 +1,6 @@
 import os
+from copy import deepcopy
+from typing import Mapping
 from unittest import mock
 from unittest.mock import Mock
 
@@ -18,6 +20,37 @@ if _FAIRSCALE_AVAILABLE:
     from fairscale.optim import OSS
 
 
+class ModelWithAdamOptimizer(BoringModel):
+    def configure_optimizers(self):
+        optimizer = torch.optim.Adam(self.layer.parameters(), lr=0.1)
+        return optimizer
+
+
+class CheckModelRestore(ModelWithAdamOptimizer):
+    def __init__(self, old_model_state_dict, old_optimizer_states):
+        super().__init__()
+        self.old_model_state_dict = old_model_state_dict
+        self.old_optimizer_states = old_optimizer_states
+
+    def on_train_start(self):
+        assert all(
+            self._is_equal(actual, expected) for actual, expected in zip(self.state_dict(), self.old_model_state_dict)
+        )
+
+        for optimizer, state in zip(self.trainer.optimizers, self.old_optimizer_states):
+            optimizer_state = self.trainer.strategy.optimizer_state(optimizer)
+            self._is_equal(optimizer_state, state)
+
+    def _is_equal(self, a, b):
+        if isinstance(a, torch.Tensor):
+            return torch.allclose(a, b)
+
+        if isinstance(a, Mapping):
+            return all(self._is_equal(a.get(k, None), b.get(k, None)) for k in b.keys())
+
+        return a == b
+
+
 @pytest.mark.parametrize("clip_val", [0, 10])
 @RunIf(min_cuda_gpus=1, fairscale=True)
 @mock.patch("fairscale.optim.oss.OSS.clip_grad_norm")
@@ -324,3 +357,33 @@ def test_ddp_sharded_strategy_checkpoint_multi_gpu_fairscale_optimizer(tmpdir, s
     # Assert model parameters are identical after loading
     for trained_param, loaded_param in zip(model.parameters(), saved_model.parameters()):
         assert torch.equal(trained_param.to("cpu"), loaded_param)
+
+
+@RunIf(min_cuda_gpus=2, fairscale=True)
+def test_ddp_sharded_strategy_fit_ckpt_path_downsize_gpus(tmpdir):
+    model = ModelWithAdamOptimizer()
+    trainer = Trainer(
+        strategy="ddp_sharded_spawn",
+        max_epochs=1,
+        limit_train_batches=1,
+        limit_val_batches=0,
+        accelerator="gpu",
+        devices=2,
+    )
+    trainer.fit(model)
+
+    checkpoint_path = trainer.checkpoint_callback.best_model_path
+    ckpt = torch.load(checkpoint_path)
+    old_model_state_dict = deepcopy(ckpt["state_dict"])
+    old_optimizer_states = deepcopy(ckpt["optimizer_states"])
+
+    model = CheckModelRestore(old_model_state_dict, old_optimizer_states)
+    trainer = Trainer(
+        strategy="ddp_sharded_spawn",
+        max_epochs=2,
+        limit_train_batches=1,
+        limit_val_batches=0,
+        accelerator="gpu",
+        devices=1,
+    )
+    trainer.fit(model, ckpt_path=checkpoint_path)