diff --git a/docsrc/index.rst b/docsrc/index.rst
index b4ede94404..5d88c8ecae 100644
--- a/docsrc/index.rst
+++ b/docsrc/index.rst
@@ -66,6 +66,7 @@ Tutorials
 * :ref:`converter_overloading`
 * :ref:`custom_kernel_plugins`
 * :ref:`mutable_torchtrt_module_example`
+* :ref:`weight_streaming_example`
 
 .. toctree::
    :caption: Tutorials
@@ -82,6 +83,7 @@ Tutorials
    tutorials/_rendered_examples/dynamo/converter_overloading
    tutorials/_rendered_examples/dynamo/custom_kernel_plugins
    tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example
+   tutorials/_rendered_examples/dynamo/weight_streaming_example
 
 Dynamo Frontend
 ----------------
diff --git a/examples/dynamo/weight_streaming_example.py b/examples/dynamo/weight_streaming_example.py
index f88e16b2ea..6ab1bb182b 100644
--- a/examples/dynamo/weight_streaming_example.py
+++ b/examples/dynamo/weight_streaming_example.py
@@ -62,6 +62,7 @@ def time_generate(model, inputs, output_seq_length, iterations=10):
     return time_mean_ms
 
 
+# Load the LLaMA-2 model
 DEVICE = torch.device("cuda:0")
 llama_path = "meta-llama/Llama-2-7b-chat-hf"
 with torch.no_grad():
@@ -69,13 +70,18 @@ def time_generate(model, inputs, output_seq_length, iterations=10):
         llama_path, use_cache=False, attn_implementation="eager"
     ).eval()
 
+# Set input and output sequence lengths
 isl = 128
 osl = 256
 
+# Create random input tensors
 input_tensors = [torch.randint(0, 5, (1, isl), dtype=torch.int64).cuda()]
+# Convert the model to half precision (FP16)
 model = model.half()
 with torch.no_grad():
+    # Define a dynamic dimension for sequence length
     seq_len = torch.export.Dim("seq_len", min=1, max=osl)
+    # Export the model with dynamic shapes
     # strict=False only enables aotautograd tracing and excludes dynamo.
     llama2_ep = torch.export.export(
         model, tuple(input_tensors), dynamic_shapes=({1: seq_len},), strict=False
@@ -89,6 +95,8 @@ def time_generate(model, inputs, output_seq_length, iterations=10):
 # the engine with weight streaming feature. use_explicit_typing=True option creates a
 # `strongly typed network <https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#strongly-typed-networks>`_ and only float32 precision is allowed in enabled_precisions option
 #
+
+# Create a TensorRT-compiled model
 trt_model = torch_tensorrt.dynamo.compile(
     llama2_ep,
     inputs=input_tensors,
@@ -106,11 +114,15 @@ def time_generate(model, inputs, output_seq_length, iterations=10):
 # Running with automatic budget size
 # ----------------------------------
 #
-# Once you specify the enable_weight_streaming option, automatic budget size is configured.
+# Once you specify the enable_weight_streaming compile option, automatic budget size is configured.
 # This automatic size may not always provide the optimal solution because the automatically determined
 # budget lacks insight into the user's specific memory constraints and usage patterns
+
+# Weight streaming context to get current weight budget information
 weight_streaming_ctx = torch_tensorrt.runtime.weight_streaming(trt_model)
+# Measure the mean latency of the model with weight streaming
 mean_latency = time_generate(trt_model, input_tensors, osl, 1)
+# Calculate the percentage of current weight budget used
 weight_budget_pct = (
     weight_streaming_ctx.device_budget / weight_streaming_ctx.total_device_budget * 100
 )
@@ -128,15 +140,19 @@ def time_generate(model, inputs, output_seq_length, iterations=10):
 # equal to ctx.total_device_budget will disable weight streaming.
 # If multiple trt engines are created, budgets are distributed proportionally
 
+# Use a context manager for weight streaming
 with torch_tensorrt.runtime.weight_streaming(trt_model) as weight_streaming_ctx:
-    # The size of the streamable weights in the engine
+    # Get the total size of streamable weights in the engine
     streamable_budget = weight_streaming_ctx.total_device_budget
 
-    # get automatic weight streaming budget size by using get_automatic_weight_streaming_budget
+    # Scenario 1: Automatic weight streaming budget
+    # Get the automatically determined weight streaming budget
     requested_budget = weight_streaming_ctx.get_automatic_weight_streaming_budget()
-    # Set and get the current weight streaming budget for inference
+    # Set the device budget to the automatically determined value
     weight_streaming_ctx.device_budget = requested_budget
+    # Measure the mean latency with automatic budget
     mean_latency = time_generate(trt_model, input_tensors, osl, 1)
+    # Calculate the percentage of the weight budget used
     weight_budget_pct = (
         weight_streaming_ctx.device_budget
         / weight_streaming_ctx.total_device_budget
@@ -146,10 +162,13 @@ def time_generate(model, inputs, output_seq_length, iterations=10):
         f"Set auto weight streaming budget as {weight_budget_pct}%. {weight_streaming_ctx.device_budget} bytes out of {weight_streaming_ctx.total_device_budget}. mean latency = {mean_latency} ms"
     )
 
-    # Set 10% of weight streaming budget
+    # Scenario 2: Manual 10% weight streaming budget
+    # Set the budget to 10% of the total streamable weights
     requested_budget = int(streamable_budget * 0.1)
     weight_streaming_ctx.device_budget = requested_budget
+    # Measure the mean latency with 10% budget
     mean_latency = time_generate(trt_model, input_tensors, osl, 1)
+    # Calculate the percentage of the weight budget used
     weight_budget_pct = (
         weight_streaming_ctx.device_budget
         / weight_streaming_ctx.total_device_budget