diff --git a/litellm/proxy/tests/load_test_embedding_100.py b/litellm/proxy/tests/load_test_embedding_100.py
new file mode 100644
index 00000000000..9fa7b8d1243
--- /dev/null
+++ b/litellm/proxy/tests/load_test_embedding_100.py
@@ -0,0 +1,58 @@
+import time, asyncio
+from openai import AsyncOpenAI
+import uuid
+import traceback
+
+
+litellm_client = AsyncOpenAI(
+    api_key="test",
+    base_url="http://0.0.0.0:8000"
+)
+
+
+
+async def litellm_completion():
+    # Your existing code for litellm_completion goes here
+    try:
+        print("starting embedding calls")
+        response  = await litellm_client.embeddings.create(
+            model="text-embedding-ada-002",
+            input = [
+                "hello who are you" * 2000, 
+                "hello who are you tomorrow 1234" * 1000,
+                "hello who are you tomorrow 1234" * 1000
+            ]  
+        )
+        print(response)
+        return response
+
+    except Exception as e:
+        # If there's an exception, log the error message
+        with open("error_log.txt", "a") as error_log:
+            error_log.write(f"Error during completion: {str(e)}\n")
+        pass
+    
+
+
+async def main():
+    start = time.time()
+    n = 10  # Number of concurrent tasks
+    tasks = [litellm_completion() for _ in range(n)]
+
+    chat_completions = await asyncio.gather(*tasks)
+
+    successful_completions = [c for c in chat_completions if c is not None]
+
+    # Write errors to error_log.txt
+    with open("error_log.txt", "a") as error_log:
+        for completion in chat_completions:
+            if isinstance(completion, str):
+                error_log.write(completion + "\n")
+
+    print(n, time.time() - start, len(successful_completions))
+
+if __name__ == "__main__":
+    # Blank out contents of error_log.txt
+    open("error_log.txt", "w").close()
+
+    asyncio.run(main())