diff --git a/llama_stack/providers/remote/inference/nvidia/nvidia.py b/llama_stack/providers/remote/inference/nvidia/nvidia.py
index 81751e038..1395caf69 100644
--- a/llama_stack/providers/remote/inference/nvidia/nvidia.py
+++ b/llama_stack/providers/remote/inference/nvidia/nvidia.py
@@ -186,7 +186,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
 
         await check_health(self._config)  # this raises errors
 
-        request = convert_chat_completion_request(
+        request = await convert_chat_completion_request(
             request=ChatCompletionRequest(
                 model=self.get_provider_model_id(model_id),
                 messages=messages,
diff --git a/llama_stack/providers/remote/inference/nvidia/openai_utils.py b/llama_stack/providers/remote/inference/nvidia/openai_utils.py
index 655d70282..40228a4da 100644
--- a/llama_stack/providers/remote/inference/nvidia/openai_utils.py
+++ b/llama_stack/providers/remote/inference/nvidia/openai_utils.py
@@ -4,10 +4,8 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import base64
 import json
 import warnings
-from io import BytesIO
 from typing import Any, AsyncGenerator, Dict, Generator, Iterable, List, Optional, Union
 
 from llama_models.datatypes import (
@@ -46,8 +44,6 @@ from openai.types.chat.chat_completion_message_tool_call_param import (
 from openai.types.completion import Completion as OpenAICompletion
 from openai.types.completion_choice import Logprobs as OpenAICompletionLogprobs
 
-from PIL import Image
-
 from llama_stack.apis.common.content_types import (
     ImageContentItem,
     InterleavedContent,
@@ -74,6 +70,10 @@ from llama_stack.apis.inference import (
     UserMessage,
 )
 
+from llama_stack.providers.utils.inference.prompt_adapter import (
+    convert_image_content_to_url,
+)
+
 
 def _convert_tooldef_to_openai_tool(tool: ToolDefinition) -> dict:
     """
@@ -151,7 +151,7 @@ def _convert_tooldef_to_openai_tool(tool: ToolDefinition) -> dict:
     return out
 
 
-def _convert_message(message: Message | Dict) -> OpenAIChatCompletionMessage:
+async def _convert_message(message: Message | Dict) -> OpenAIChatCompletionMessage:
     """
     Convert a Message to an OpenAI API-compatible dictionary.
     """
@@ -177,36 +177,21 @@ def _convert_message(message: Message | Dict) -> OpenAIChatCompletionMessage:
     #  {"type": "image", "image": {"url": {"uri": ...}}} -> {"type": "image_url", "image_url": {"url": ...}}
     #  {"type": "image", "image": {"data": ...}} -> {"type": "image_url", "image_url": {"url": "data:image/?;base64,..."}}
     #  List[...] -> List[...]
-    def _convert_user_message_content(
+    async def _convert_user_message_content(
         content: InterleavedContent,
     ) -> Union[str, Iterable[OpenAIChatCompletionContentPartParam]]:
         # Llama Stack and OpenAI spec match for str and text input
         if isinstance(content, str) or isinstance(content, TextContentItem):
             return content
         elif isinstance(content, ImageContentItem):
-            if content.image.url:
-                return OpenAIChatCompletionContentPartImageParam(
-                    image_url=OpenAIImageURL(url=content.image.url.uri),
-                    type="image_url",
-                )
-            elif content.image.data:
-                mime_type = Image.MIME[
-                    Image.open(
-                        BytesIO(
-                            base64.b64decode(
-                                content.image.data
-                            )  # TODO(mf): do this more efficiently, decode less
-                        )
-                    ).format
-                ]
-                return OpenAIChatCompletionContentPartImageParam(
-                    image_url=OpenAIImageURL(
-                        url=f"data:{mime_type};base64,{content.image.data}"
-                    ),
-                    type="image_url",
-                )
+            return OpenAIChatCompletionContentPartImageParam(
+                image_url=OpenAIImageURL(
+                    url=await convert_image_content_to_url(content)
+                ),
+                type="image_url",
+            )
         elif isinstance(content, List):
-            return [_convert_user_message_content(item) for item in content]
+            return [await _convert_user_message_content(item) for item in content]
         else:
             raise ValueError(f"Unsupported content type: {type(content)}")
 
@@ -214,7 +199,7 @@ def _convert_message(message: Message | Dict) -> OpenAIChatCompletionMessage:
     if isinstance(message, UserMessage):
         out = OpenAIChatCompletionUserMessage(
             role="user",
-            content=_convert_user_message_content(message.content),
+            content=await _convert_user_message_content(message.content),
         )
     elif isinstance(message, CompletionMessage):
         out = OpenAIChatCompletionAssistantMessage(
@@ -249,7 +234,7 @@ def _convert_message(message: Message | Dict) -> OpenAIChatCompletionMessage:
     return out
 
 
-def convert_chat_completion_request(
+async def convert_chat_completion_request(
     request: ChatCompletionRequest,
     n: int = 1,
 ) -> dict:
@@ -286,7 +271,7 @@ def convert_chat_completion_request(
     nvext = {}
     payload: Dict[str, Any] = dict(
         model=request.model,
-        messages=[_convert_message(message) for message in request.messages],
+        messages=[await _convert_message(message) for message in request.messages],
         stream=request.stream,
         n=n,
         extra_body=dict(nvext=nvext),
diff --git a/llama_stack/providers/utils/inference/prompt_adapter.py b/llama_stack/providers/utils/inference/prompt_adapter.py
index e49771980..89a41e97d 100644
--- a/llama_stack/providers/utils/inference/prompt_adapter.py
+++ b/llama_stack/providers/utils/inference/prompt_adapter.py
@@ -186,6 +186,7 @@ async def localize_image_content(media: ImageContentItem) -> Tuple[bytes, str]:
         return content, format
     else:
         # data is a base64 encoded string, decode it to bytes first
+        # TODO(mf): do this more efficiently, decode less
         data_bytes = base64.b64decode(image.data)
         pil_image = PIL_Image.open(io.BytesIO(data_bytes))
         return data_bytes, pil_image.format