diff --git a/tests/entrypoints/openai/test_prompt_validation.py b/tests/entrypoints/openai/test_prompt_validation.py
index e31a1d077608f..4197583074dfe 100644
--- a/tests/entrypoints/openai/test_prompt_validation.py
+++ b/tests/entrypoints/openai/test_prompt_validation.py
@@ -1,10 +1,16 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import io
+
 # imports for guided decoding tests
 import openai
+import pybase64
 import pytest
 import regex as re
+import torch
+
+from vllm.entrypoints.openai.serving_engine import OpenAIServing
 
 from ...utils import RemoteOpenAIServer
 
@@ -42,3 +48,46 @@ async def test_out_of_vocab_token_ids():
                                             prompt=[999999],
                                             max_tokens=5,
                                             temperature=0.0)
+
+
+@pytest.mark.parametrize("dtype",
+                         [torch.float32, torch.bfloat16, torch.float16])
+@pytest.mark.parametrize(
+    "layout",
+    [torch.strided, torch.sparse_coo, torch.sparse_csc, torch.sparse_csr])
+@pytest.mark.parametrize("seq_len", [2, 10])
+@pytest.mark.parametrize("hidden_size", [2, 10])
+def test_load_prompt_embeds(dtype: torch.dtype, layout: torch.layout,
+                            seq_len: int, hidden_size: int):
+    # construct arbitrary tensors of various dtypes, layouts, and sizes.
+    # We need to check against different layouts to make sure that if a user
+    # uses sparse tensors to reduce the transmission size of prompt embeddings,
+    # we must cast them to dense/strided before passing them into the engine.
+    # We don't use non-CPU tensors in this test to avoid preemptively
+    # initializing cuda and break other tests in the suite that fork processes.
+    # We also need to make sure that we only use devices that are actually
+    # available in the environment the test is running on. For simplicity,
+    # we just test against CPU.
+    tensor = torch.randn((seq_len, hidden_size), dtype=dtype)
+    if layout == torch.strided:
+        tensor = tensor.contiguous()
+    elif layout == torch.sparse_coo:
+        tensor = tensor.to_sparse_coo()
+    elif layout == torch.sparse_csc:
+        tensor = tensor.to_sparse_csc()
+    elif layout == torch.sparse_csr:
+        tensor = tensor.to_sparse_csr()
+
+    buffer = io.BytesIO()
+    torch.save(tensor, buffer)
+    buffer.seek(0)
+    encoded_tensor = pybase64.b64encode(buffer.getvalue())
+
+    loaded_prompt_embeds = OpenAIServing._load_prompt_embeds(encoded_tensor)
+    assert len(loaded_prompt_embeds) == 1
+    loaded_tensor = loaded_prompt_embeds[0]["prompt_embeds"]
+    assert loaded_tensor.device.type == "cpu"
+    assert loaded_tensor.layout == torch.strided
+    torch.testing.assert_close(loaded_tensor,
+                               tensor.to("cpu").to_dense(),
+                               equal_nan=True)
diff --git a/vllm/entrypoints/openai/serving_engine.py b/vllm/entrypoints/openai/serving_engine.py
index d6f92a63301e8..0f4a7c0186b65 100644
--- a/vllm/entrypoints/openai/serving_engine.py
+++ b/vllm/entrypoints/openai/serving_engine.py
@@ -1006,8 +1006,8 @@ class OpenAIServing:
             # OPTIMIZATION
             priority = orig_priority - 1
 
+    @staticmethod
     def _load_prompt_embeds(
-        self,
         prompt_embeds: Optional[Union[bytes, list[bytes]]],
         truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None
     ) -> list[EmbedsPrompt]:
@@ -1015,12 +1015,14 @@ class OpenAIServing:
         def _load_and_validate_embed(embed: bytes) -> EmbedsPrompt:
             tensor = torch.load(io.BytesIO(
                 pybase64.b64decode(embed, validate=True)),
-                                weights_only=True)
+                                weights_only=True,
+                                map_location=torch.device("cpu"))
             assert isinstance(tensor, torch.Tensor) and tensor.dtype in (
                 torch.float32,
                 torch.bfloat16,
                 torch.float16,
             )
+            tensor = tensor.to_dense()
             if tensor.dim() > 2:
                 tensor = tensor.squeeze(0)
                 assert tensor.dim() == 2