diff --git a/06_gpu_and_ml/llm-serving/trtllm_llama.py b/06_gpu_and_ml/llm-serving/trtllm_llama.py
index 1fb053484..b91a4f570 100644
--- a/06_gpu_and_ml/llm-serving/trtllm_llama.py
+++ b/06_gpu_and_ml/llm-serving/trtllm_llama.py
@@ -43,7 +43,7 @@
 #
 # To run TensorRT-LLM, we must first install it. Easier said than done!
 #
-# In Modal, we define [container images](https://modal.com/docs/guide/custom-containers) that run our serverless workloads.
+# In Modal, we define [container images](https://modal.com/docs/guide/custom-container) that run our serverless workloads.
 # All Modal containers have access to GPU drivers via the underlying host environment,
 # but we still need to install the software stack on top of the drivers, from the CUDA runtime up.
 #
diff --git a/06_gpu_and_ml/tensorflow/tensorflow_tutorial.py b/06_gpu_and_ml/tensorflow/tensorflow_tutorial.py
index ba141c163..c0659b097 100644
--- a/06_gpu_and_ml/tensorflow/tensorflow_tutorial.py
+++ b/06_gpu_and_ml/tensorflow/tensorflow_tutorial.py
@@ -15,7 +15,7 @@
 #
 # Configuring a system to properly run GPU-accelerated TensorFlow can be challenging.
 # Luckily, Modal makes it easy to stand on the shoulders of giants and
-# [use a pre-built Docker container image](https://modal.com/docs/guide/custom-containers#use-an-existing-container-image-with-from_registry) from a registry like Docker Hub.
+# [use a pre-built Docker container image](https://modal.com/docs/guide/custom-container#use-an-existing-container-image-with-from_registry) from a registry like Docker Hub.
 # We recommend TensorFlow's [official base Docker container images](https://hub.docker.com/r/tensorflow/tensorflow), which come with `tensorflow` and its matching CUDA libraries already installed.
 #
 # If you want to install TensorFlow some other way, check out [their docs](https://www.tensorflow.org/install) for options and instructions.