diff --git a/components/google-cloud/RELEASE.md b/components/google-cloud/RELEASE.md
index 38b84d88bf0..fc40e16bf96 100644
--- a/components/google-cloud/RELEASE.md
+++ b/components/google-cloud/RELEASE.md
@@ -6,6 +6,7 @@
 * Add sliced evaluation metrics support for custom and unstructured AutoML models in evaluation pipeline and evaluation pipeline with feature attribution.
 * Support `service_account` in `ModelBatchPredictOp`.
 * Release `DataflowFlexTemplateJobOp` to GA namespace (`v1.dataflow.DataflowFlexTemplateJobOp`).
+* Make `model_checkpoint` optional for `preview.llm.infer_pipeline`. If not provided, the base model associated with the `large_model_reference` will be used.
 
 ## Release 2.4.1
 * Disable caching for LLM pipeline tasks that store temporary artifacts.
diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/llm/infer/component.py b/components/google-cloud/google_cloud_pipeline_components/preview/llm/infer/component.py
index cfa0f715568..429916e4346 100644
--- a/components/google-cloud/google_cloud_pipeline_components/preview/llm/infer/component.py
+++ b/components/google-cloud/google_cloud_pipeline_components/preview/llm/infer/component.py
@@ -33,8 +33,8 @@
 )
 def infer_pipeline(
     large_model_reference: str,
-    model_checkpoint: str,
     prompt_dataset: str,
+    model_checkpoint: Optional[str] = None,
     prompt_sequence_length: int = 512,
     target_sequence_length: int = 64,
     sampling_strategy: str = 'greedy',
@@ -47,7 +47,7 @@ def infer_pipeline(
 
   Args:
     large_model_reference: Name of the base model. Supported values are `text-bison@001`, `t5-small`, `t5-large`, `t5-xl` and `t5-xxl`. `text-bison@001` and `t5-small` are supported in `us-central1` and `europe-west4`. `t5-large`, `t5-xl` and `t5-xxl` are only supported in `europe-west4`.
-    model_checkpoint: Cloud storage path to the model checkpoint.
+    model_checkpoint: Optional Cloud storage path to the model checkpoint. If not provided, the default checkpoint for the `large_model_reference` will be used.
     prompt_dataset: Cloud storage path to an unlabled prompt dataset used for reinforcement learning. The dataset format is jsonl. Each example in the dataset must have an `input_text` field that contains the prompt.
     prompt_sequence_length: Maximum tokenized sequence length for input text. Higher values increase memory overhead. This value should be at most 8192. Default value is 512.
     target_sequence_length:  Maximum tokenized sequence length for target text. Higher values increase memory overhead. This value should be at most 1024. Default value is 64.
@@ -66,7 +66,8 @@ def infer_pipeline(
       use_test_spec=env.get_use_test_machine_spec(),
   ).set_display_name('Resolve Machine Spec')
   reference_model_metadata = function_based.resolve_reference_model_metadata(
-      large_model_reference=large_model_reference
+      large_model_reference=large_model_reference,
+      reference_model_path=model_checkpoint,
   ).set_display_name('Resolve Model Metadata')
 
   prompt_dataset_image_uri = function_based.resolve_private_image_uri(
@@ -98,7 +99,7 @@ def infer_pipeline(
   bulk_inference = bulk_inferrer.BulkInferrer(
       project=project,
       location=location,
-      input_model=model_checkpoint,
+      input_model=reference_model_metadata.outputs['reference_model_path'],
       input_dataset_path=prompt_dataset_importer.outputs['imported_data_path'],
       dataset_split=env.TRAIN_SPLIT,
       inputs_sequence_length=prompt_sequence_length,