diff --git a/tests/local_testing/test_provider_specific_config.py b/tests/local_testing/test_provider_specific_config.py
index a6bad688201..25320f2080f 100644
--- a/tests/local_testing/test_provider_specific_config.py
+++ b/tests/local_testing/test_provider_specific_config.py
@@ -12,36 +12,6 @@ from unittest.mock import AsyncMock, MagicMock, patch
 import litellm
 from litellm import RateLimitError, completion
 
-#  Huggingface - Expensive to deploy models and keep them running. Maybe we can try doing this via baseten??
-# def hf_test_completion_tgi():
-#     litellm.HuggingfaceConfig(max_new_tokens=200)
-#     litellm.set_verbose=True
-#     try:
-#         # OVERRIDE WITH DYNAMIC MAX TOKENS
-#         response_1 = litellm.completion(
-#             model="huggingface/mistralai/Mistral-7B-Instruct-v0.1",
-#             messages=[{ "content": "Hello, how are you?","role": "user"}],
-#             api_base="https://n9ox93a8sv5ihsow.us-east-1.aws.endpoints.huggingface.cloud",
-#             max_tokens=10
-#         )
-#         # Add any assertions here to check the response
-#         print(response_1)
-#         response_1_text = response_1.choices[0].message.content
-
-#         # USE CONFIG TOKENS
-#         response_2 = litellm.completion(
-#             model="huggingface/mistralai/Mistral-7B-Instruct-v0.1",
-#             messages=[{ "content": "Hello, how are you?","role": "user"}],
-#             api_base="https://n9ox93a8sv5ihsow.us-east-1.aws.endpoints.huggingface.cloud",
-#         )
-#         # Add any assertions here to check the response
-#         print(response_2)
-#         response_2_text = response_2.choices[0].message.content
-
-#         assert len(response_2_text) > len(response_1_text)
-#     except Exception as e:
-#         pytest.fail(f"Error occurred: {e}")
-# hf_test_completion_tgi()
 
 # Anthropic
 
@@ -322,65 +292,6 @@ def aleph_alpha_test_completion():
 
 # aleph_alpha_test_completion()
 
-#  Petals - calls are too slow, will cause circle ci to fail due to delay. Test locally.
-# def petals_completion():
-#     litellm.PetalsConfig(max_new_tokens=10)
-#     # litellm.set_verbose=True
-#     try:
-#         # OVERRIDE WITH DYNAMIC MAX TOKENS
-#         response_1 = litellm.completion(
-#             model="petals/petals-team/StableBeluga2",
-#             messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}],
-#             api_base="https://chat.petals.dev/api/v1/generate",
-#             max_tokens=100
-#         )
-#         response_1_text = response_1.choices[0].message.content
-#         print(f"response_1_text: {response_1_text}")
-
-#         # USE CONFIG TOKENS
-#         response_2 = litellm.completion(
-#             model="petals/petals-team/StableBeluga2",
-#             api_base="https://chat.petals.dev/api/v1/generate",
-#             messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}],
-#         )
-#         response_2_text = response_2.choices[0].message.content
-#         print(f"response_2_text: {response_2_text}")
-
-#         assert len(response_2_text) < len(response_1_text)
-#     except Exception as e:
-#         pytest.fail(f"Error occurred: {e}")
-
-# petals_completion()
-
-#  VertexAI
-# We don't have vertex ai configured for circle ci yet -- need to figure this out.
-# def vertex_ai_test_completion():
-#     litellm.VertexAIConfig(max_output_tokens=10)
-#     # litellm.set_verbose=True
-#     try:
-#         # OVERRIDE WITH DYNAMIC MAX TOKENS
-#         response_1 = litellm.completion(
-#             model="chat-bison",
-#             messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}],
-#             max_tokens=100
-#         )
-#         response_1_text = response_1.choices[0].message.content
-#         print(f"response_1_text: {response_1_text}")
-
-#         # USE CONFIG TOKENS
-#         response_2 = litellm.completion(
-#             model="chat-bison",
-#             messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}],
-#         )
-#         response_2_text = response_2.choices[0].message.content
-#         print(f"response_2_text: {response_2_text}")
-
-#         assert len(response_2_text) < len(response_1_text)
-#     except Exception as e:
-#         pytest.fail(f"Error occurred: {e}")
-
-# vertex_ai_test_completion()
-
 #  Sagemaker