diff --git a/tests/entrypoints/openai/test_embedding.py b/tests/entrypoints/openai/test_embedding.py
index 82a5627aa1d63..7c7232dbccaa7 100644
--- a/tests/entrypoints/openai/test_embedding.py
+++ b/tests/entrypoints/openai/test_embedding.py
@@ -1,3 +1,6 @@
+import base64
+
+import numpy as np
 import openai
 import pytest
 import ray
@@ -109,3 +112,33 @@ async def test_batch_embedding(embedding_client: openai.AsyncOpenAI,
     assert embeddings.usage.completion_tokens == 0
     assert embeddings.usage.prompt_tokens == 17
     assert embeddings.usage.total_tokens == 17
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "model_name",
+    [EMBEDDING_MODEL_NAME],
+)
+async def test_batch_base64_embedding(embedding_client: openai.AsyncOpenAI,
+                                      model_name: str):
+    input_texts = [
+        "Hello my name is",
+        "The best thing about vLLM is that it supports many different models"
+    ]
+
+    responses_float = await embedding_client.embeddings.create(
+        input=input_texts, model=model_name, encoding_format="float")
+
+    responses_base64 = await embedding_client.embeddings.create(
+        input=input_texts, model=model_name, encoding_format="base64")
+
+    decoded_responses_base64_data = []
+    for data in responses_base64.data:
+        decoded_responses_base64_data.append(
+            np.frombuffer(base64.b64decode(data.embedding),
+                          dtype="float").tolist())
+
+    assert responses_float.data[0].embedding == decoded_responses_base64_data[
+        0]
+    assert responses_float.data[1].embedding == decoded_responses_base64_data[
+        1]
diff --git a/vllm/entrypoints/openai/protocol.py b/vllm/entrypoints/openai/protocol.py
index 0ad46cbea2ce6..d1568cb3a773c 100644
--- a/vllm/entrypoints/openai/protocol.py
+++ b/vllm/entrypoints/openai/protocol.py
@@ -580,7 +580,7 @@ class CompletionStreamResponse(OpenAIBaseModel):
 class EmbeddingResponseData(BaseModel):
     index: int
     object: str = "embedding"
-    embedding: List[float]
+    embedding: Union[List[float], str]
 
 
 class EmbeddingResponse(BaseModel):
diff --git a/vllm/entrypoints/openai/serving_embedding.py b/vllm/entrypoints/openai/serving_embedding.py
index cbf09f173fb66..4838cb7d0255a 100644
--- a/vllm/entrypoints/openai/serving_embedding.py
+++ b/vllm/entrypoints/openai/serving_embedding.py
@@ -1,6 +1,8 @@
+import base64
 import time
 from typing import AsyncIterator, List, Optional, Tuple
 
+import numpy as np
 from fastapi import Request
 
 from vllm.config import ModelConfig
@@ -20,19 +22,18 @@
 
 
 def request_output_to_embedding_response(
-    final_res_batch: List[EmbeddingRequestOutput],
-    request_id: str,
-    created_time: int,
-    model_name: str,
-) -> EmbeddingResponse:
+        final_res_batch: List[EmbeddingRequestOutput], request_id: str,
+        created_time: int, model_name: str,
+        encoding_format: str) -> EmbeddingResponse:
     data: List[EmbeddingResponseData] = []
     num_prompt_tokens = 0
     for idx, final_res in enumerate(final_res_batch):
         assert final_res is not None
         prompt_token_ids = final_res.prompt_token_ids
-
-        embedding_data = EmbeddingResponseData(
-            index=idx, embedding=final_res.outputs.embedding)
+        embedding = final_res.outputs.embedding
+        if encoding_format == "base64":
+            embedding = base64.b64encode(np.array(embedding))
+        embedding_data = EmbeddingResponseData(index=idx, embedding=embedding)
         data.append(embedding_data)
 
         num_prompt_tokens += len(prompt_token_ids)
@@ -72,10 +73,8 @@ async def create_embedding(self, request: EmbeddingRequest,
         if error_check_ret is not None:
             return error_check_ret
 
-        # Return error for unsupported features.
-        if request.encoding_format == "base64":
-            return self.create_error_response(
-                "base64 encoding is not currently supported")
+        encoding_format = (request.encoding_format
+                           if request.encoding_format else "float")
         if request.dimensions is not None:
             return self.create_error_response(
                 "dimensions is currently not supported")
@@ -129,7 +128,8 @@ async def create_embedding(self, request: EmbeddingRequest,
                     return self.create_error_response("Client disconnected")
                 final_res_batch[i] = res
             response = request_output_to_embedding_response(
-                final_res_batch, request_id, created_time, model_name)
+                final_res_batch, request_id, created_time, model_name,
+                encoding_format)
         except ValueError as e:
             # TODO: Use a vllm-specific Validation Error
             return self.create_error_response(str(e))