diff --git a/llama_stack/providers/remote/inference/ollama/ollama.py b/llama_stack/providers/remote/inference/ollama/ollama.py
index 358a29d4c..58f92c829 100644
--- a/llama_stack/providers/remote/inference/ollama/ollama.py
+++ b/llama_stack/providers/remote/inference/ollama/ollama.py
@@ -33,7 +33,6 @@ from llama_stack.apis.inference import (
     JsonSchemaResponseFormat,
     LogProbConfig,
     Message,
-    OpenAIEmbeddingsResponse,
     ResponseFormat,
     SamplingParams,
     TextTruncation,
@@ -46,6 +45,9 @@ from llama_stack.apis.inference.inference import (
     OpenAIChatCompletion,
     OpenAIChatCompletionChunk,
     OpenAICompletion,
+    OpenAIEmbeddingData,
+    OpenAIEmbeddingsResponse,
+    OpenAIEmbeddingUsage,
     OpenAIMessageParam,
     OpenAIResponseFormatParam,
 )
@@ -386,7 +388,42 @@ class OllamaInferenceAdapter(
         dimensions: int | None = None,
         user: str | None = None,
     ) -> OpenAIEmbeddingsResponse:
-        raise NotImplementedError()
+        model_obj = await self._get_model(model)
+        if model_obj.model_type != ModelType.embedding:
+            raise ValueError(f"Model {model} is not an embedding model")
+
+        params = {
+            "model": model_obj.provider_resource_id,
+            "input": input,
+        }
+
+        # Note, at the moment Ollama does not support encoding_format, dimensions, and user parameters
+        if encoding_format is not None:
+            params["encoding_format"] = encoding_format
+        if dimensions is not None:
+            params["dimensions"] = str(dimensions)
+        if user is not None:
+            params["user"] = user
+
+        response = await self.openai_client.embeddings.create(**params)
+        data = []
+        for i, embedding_data in enumerate(response.data):
+            data.append(
+                OpenAIEmbeddingData(
+                    embedding=embedding_data.embedding,
+                    index=i,
+                )
+            )
+
+        usage = OpenAIEmbeddingUsage(
+            prompt_tokens=response.usage.prompt_tokens,
+            total_tokens=response.usage.total_tokens,
+        )
+        return OpenAIEmbeddingsResponse(
+            data=data,
+            model=response.model,
+            usage=usage,
+        )
 
     async def openai_completion(
         self,
diff --git a/tests/integration/inference/test_openai_embeddings.py b/tests/integration/inference/test_openai_embeddings.py
index 90a91a206..1b8bd9038 100644
--- a/tests/integration/inference/test_openai_embeddings.py
+++ b/tests/integration/inference/test_openai_embeddings.py
@@ -51,7 +51,6 @@ def skip_if_model_doesnt_support_openai_embeddings(client, model_id):
         "remote::runpod",
         "remote::sambanova",
         "remote::tgi",
-        "remote::ollama",
     ):
         pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI embeddings.")