diff --git a/src/pytorch_lightning/CHANGELOG.md b/src/pytorch_lightning/CHANGELOG.md
index f2a4689ca2d20..5f687de11a8da 100644
--- a/src/pytorch_lightning/CHANGELOG.md
+++ b/src/pytorch_lightning/CHANGELOG.md
@@ -179,6 +179,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Do not update on-plateau schedulers when reloading from an end-of-epoch checkpoint ([#14702](https://github.com/Lightning-AI/lightning/pull/14702))
 - Fixed `Trainer` support for PyTorch built without distributed support ([#14971](https://github.com/Lightning-AI/lightning/pull/14971))
 - Fixed batch normalization statistics calculation in `StochasticWeightAveraging` callback ([#14866](https://github.com/Lightning-AI/lightning/pull/14866))
+- Avoided initializing optimizers during deepspeed inference ([#14944](https://github.com/Lightning-AI/lightning/pull/14944))
 - Fixed `LightningCLI` parse_env and description in subcommands ([#15138](https://github.com/Lightning-AI/lightning/pull/15138))
 
 
diff --git a/src/pytorch_lightning/strategies/deepspeed.py b/src/pytorch_lightning/strategies/deepspeed.py
index d7e4cb500665f..ec6d2fc3fad67 100644
--- a/src/pytorch_lightning/strategies/deepspeed.py
+++ b/src/pytorch_lightning/strategies/deepspeed.py
@@ -559,17 +559,8 @@ def _set_deepspeed_activation_checkpointing(self) -> None:
             )
 
     def _initialize_deepspeed_inference(self, model: Module) -> None:
-        # todo: Currently DeepSpeed requires optimizers at inference to partition weights correctly
         assert isinstance(self.config, dict)
-        optimizer, scheduler = None, None
-        if "optimizer" not in self.config:
-            rank_zero_info(
-                "You have not specified an optimizer or scheduler within the DeepSpeed config."
-                " Using `configure_optimizers` to define optimizer and scheduler."
-            )
-            optimizer, lr_scheduler, _ = self._init_optimizers()
-            if lr_scheduler is not None:
-                scheduler = lr_scheduler.scheduler
+
         # todo: this is required for DeepSpeed throughput timers
         inference_config = {"train_micro_batch_size_per_gpu": 1}
         if "fp16" in self.config:
@@ -587,8 +578,8 @@ def _initialize_deepspeed_inference(self, model: Module) -> None:
             args=argparse.Namespace(device_rank=self.root_device.index),
             config=inference_config,
             model=model,
-            optimizer=optimizer,
-            lr_scheduler=scheduler,
+            optimizer=None,
+            lr_scheduler=None,
             model_parameters=[],
             dist_init_required=False,
         )
diff --git a/tests/tests_pytorch/strategies/test_deepspeed_strategy.py b/tests/tests_pytorch/strategies/test_deepspeed_strategy.py
index 9b6ea83a94cf1..b31e4ee1f6a03 100644
--- a/tests/tests_pytorch/strategies/test_deepspeed_strategy.py
+++ b/tests/tests_pytorch/strategies/test_deepspeed_strategy.py
@@ -468,10 +468,16 @@ def test_deepspeed_multigpu(tmpdir):
         enable_progress_bar=False,
         enable_model_summary=False,
     )
+
+    with mock.patch.object(
+        model, "configure_optimizers", wraps=model.configure_optimizers
+    ) as mock_configure_optimizers:
+        trainer.test(model)
+    assert mock_configure_optimizers.call_count == 0
+
     with mock.patch("deepspeed.init_distributed", wraps=deepspeed.init_distributed) as mock_deepspeed_distributed:
         trainer.fit(model)
     mock_deepspeed_distributed.assert_called_once()
-    trainer.test(model)
 
     _assert_save_model_is_equal(model, tmpdir, trainer)
 
@@ -655,8 +661,8 @@ def test_deepspeed_multigpu_stage_3(tmpdir):
         enable_progress_bar=False,
         enable_model_summary=False,
     )
-    trainer.fit(model)
     trainer.test(model)
+    trainer.fit(model)
 
     _assert_save_model_is_equal(model, tmpdir, trainer)
 
@@ -676,8 +682,8 @@ def test_deepspeed_multigpu_stage_3_manual_optimization(tmpdir, deepspeed_config
         enable_progress_bar=False,
         enable_model_summary=False,
     )
-    trainer.fit(model)
     trainer.test(model)
+    trainer.fit(model)
 
     _assert_save_model_is_equal(model, tmpdir, trainer)