diff --git a/llama_stack/providers/remote/inference/nvidia/nvidia.py b/llama_stack/providers/remote/inference/nvidia/nvidia.py
index b2ad060fb..7a2697327 100644
--- a/llama_stack/providers/remote/inference/nvidia/nvidia.py
+++ b/llama_stack/providers/remote/inference/nvidia/nvidia.py
@@ -13,7 +13,6 @@ from llama_stack.apis.inference import (
     OpenAIEmbeddingUsage,
 )
 from llama_stack.log import get_logger
-from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from . import NVIDIAConfig
@@ -22,7 +21,9 @@ from .utils import _is_nvidia_hosted
 logger = get_logger(name=__name__, category="inference::nvidia")
 
 
-class NVIDIAInferenceAdapter(OpenAIMixin, ModelRegistryHelper):
+class NVIDIAInferenceAdapter(OpenAIMixin):
+    config: NVIDIAConfig
+
     """
     NVIDIA Inference Adapter for Llama Stack.
 
@@ -36,27 +37,12 @@ class NVIDIAInferenceAdapter(OpenAIMixin, ModelRegistryHelper):
     - ModelRegistryHelper.check_model_availability() just returns False and shows a warning
     """
 
-    def __init__(self, config: NVIDIAConfig) -> None:
-        """Initialize the NVIDIA inference adapter with configuration."""
-        # Initialize ModelRegistryHelper with empty model entries since NVIDIA uses dynamic model discovery
-        ModelRegistryHelper.__init__(self, model_entries=[], allowed_models=config.allowed_models)
-        self.config = config
-
     # source: https://docs.nvidia.com/nim/nemo-retriever/text-embedding/latest/support-matrix.html
     embedding_model_metadata: dict[str, dict[str, int]] = {
-        "nvidia/llama-3.2-nv-embedqa-1b-v2": {
-            "embedding_dimension": 2048,
-            "context_length": 8192,
-        },
+        "nvidia/llama-3.2-nv-embedqa-1b-v2": {"embedding_dimension": 2048, "context_length": 8192},
         "nvidia/nv-embedqa-e5-v5": {"embedding_dimension": 512, "context_length": 1024},
-        "nvidia/nv-embedqa-mistral-7b-v2": {
-            "embedding_dimension": 512,
-            "context_length": 4096,
-        },
-        "snowflake/arctic-embed-l": {
-            "embedding_dimension": 512,
-            "context_length": 1024,
-        },
+        "nvidia/nv-embedqa-mistral-7b-v2": {"embedding_dimension": 512, "context_length": 4096},
+        "snowflake/arctic-embed-l": {"embedding_dimension": 512, "context_length": 1024},
     }
 
     async def initialize(self) -> None:
@@ -109,7 +95,7 @@ class NVIDIAInferenceAdapter(OpenAIMixin, ModelRegistryHelper):
         response = await self.client.embeddings.create(
             model=await self._get_provider_model_id(model),
             input=input,
-            encoding_format=(encoding_format if encoding_format is not None else NOT_GIVEN),
+            encoding_format=encoding_format if encoding_format is not None else NOT_GIVEN,
             dimensions=dimensions if dimensions is not None else NOT_GIVEN,
             user=user if user is not None else NOT_GIVEN,
             extra_body=extra_body,