diff --git a/llama_stack/providers/remote/inference/nvidia/NVIDIA.md b/llama_stack/providers/remote/inference/nvidia/NVIDIA.md
new file mode 100644
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+++ b/llama_stack/providers/remote/inference/nvidia/NVIDIA.md
@@ -0,0 +1,85 @@
+# NVIDIA Inference Provider for LlamaStack
+
+This provider enables running inference using NVIDIA NIM.
+
+## Features
+- Endpoints for completions, chat completions, and embeddings for registered models
+
+## Getting Started
+
+### Prerequisites
+
+- LlamaStack with NVIDIA configuration
+- Access to NVIDIA NIM deployment
+- NIM for model to use for inference is deployed
+
+### Setup
+
+Build the NVIDIA environment:
+
+```bash
+llama stack build --template nvidia --image-type conda
+```
+
+### Basic Usage using the LlamaStack Python Client
+
+#### Initialize the client
+
+```python
+import os
+
+os.environ["NVIDIA_API_KEY"] = (
+    ""  # Required if using hosted NIM endpoint. If self-hosted, not required.
+)
+os.environ["NVIDIA_BASE_URL"] = "http://nim.test"  # NIM URL
+
+from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
+
+client = LlamaStackAsLibraryClient("nvidia")
+client.initialize()
+```
+
+### Create Completion
+
+```python
+response = client.completion(
+    model_id="meta-llama/Llama-3.1-8b-Instruct",
+    content="Complete the sentence using one word: Roses are red, violets are :",
+    stream=False,
+    sampling_params={
+        "max_tokens": 50,
+    },
+)
+print(f"Response: {response.content}")
+```
+
+### Create Chat Completion
+
+```python
+response = client.chat_completion(
+    model_id="meta-llama/Llama-3.1-8b-Instruct",
+    messages=[
+        {
+            "role": "system",
+            "content": "You must respond to each message with only one word",
+        },
+        {
+            "role": "user",
+            "content": "Complete the sentence using one word: Roses are red, violets are:",
+        },
+    ],
+    stream=False,
+    sampling_params={
+        "max_tokens": 50,
+    },
+)
+print(f"Response: {response.completion_message.content}")
+```
+
+### Create Embeddings
+```python
+response = client.embeddings(
+    model_id="meta-llama/Llama-3.1-8b-Instruct", contents=["foo", "bar", "baz"]
+)
+print(f"Embeddings: {response.embeddings}")
+```
diff --git a/llama_stack/providers/remote/safety/nvidia/README.md b/llama_stack/providers/remote/safety/nvidia/README.md
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+++ b/llama_stack/providers/remote/safety/nvidia/README.md
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+# NVIDIA Safety Provider for LlamaStack
+
+This provider enables safety checks and guardrails for LLM interactions using NVIDIA's NeMo Guardrails service.
+
+## Features
+
+- Run safety checks for messages
+
+## Getting Started
+
+### Prerequisites
+
+- LlamaStack with NVIDIA configuration
+- Access to NVIDIA NeMo Guardrails service
+- NIM for model to use for safety check is deployed
+
+### Setup
+
+Build the NVIDIA environment:
+
+```bash
+llama stack build --template nvidia --image-type conda
+```
+
+### Basic Usage using the LlamaStack Python Client
+
+#### Initialize the client
+
+```python
+import os
+
+os.environ["NVIDIA_API_KEY"] = "your-api-key"
+os.environ["NVIDIA_GUARDRAILS_URL"] = "http://guardrails.test"
+
+from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
+
+client = LlamaStackAsLibraryClient("nvidia")
+client.initialize()
+```
+
+#### Create a safety shield
+
+```python
+from llama_stack.apis.safety import Shield
+from llama_stack.apis.inference import Message
+
+# Create a safety shield
+shield = Shield(
+    shield_id="your-shield-id",
+    provider_resource_id="safety-model-id",  # The model to use for safety checks
+    description="Safety checks for content moderation",
+)
+
+# Register the shield
+await client.safety.register_shield(shield)
+```
+
+#### Run safety checks
+
+```python
+# Messages to check
+messages = [Message(role="user", content="Your message to check")]
+
+# Run safety check
+response = await client.safety.run_shield(
+    shield_id="your-shield-id",
+    messages=messages,
+)
+
+# Check for violations
+if response.violation:
+    print(f"Safety violation detected: {response.violation.user_message}")
+    print(f"Violation level: {response.violation.violation_level}")
+    print(f"Metadata: {response.violation.metadata}")
+else:
+    print("No safety violations detected")
+```