diff --git a/vllm/v1/worker/tpu_model_runner.py b/vllm/v1/worker/tpu_model_runner.py
index 6300f16c..c61c449e 100644
--- a/vllm/v1/worker/tpu_model_runner.py
+++ b/vllm/v1/worker/tpu_model_runner.py
@@ -53,6 +53,41 @@ INVALID_TOKEN_ID = -1
 MIN_NUM_SEQS = 8
 
 
+#########################################################
+# Ways to avoid recompilation
+#########################################################
+#
+# The model executor has two primary components:
+# 1. preparing the model and sampler inputs
+# 2. executing the model and sampler.
+# The core idea is to avoid any TPU computation during input preparation. For
+# better compilation tracking and increased flexibility, the model execution and
+# sampler are divided into several distinct components.
+#
+# Below are the detailed steps:
+#
+# Step 1
+# It is recommended to avoid TPU operations when preparing the model and sampler
+# inputs. CPU tensors can be prepared and transferred to the XLA device using
+# cpu_tensor.to(xla_device), which only triggers CPU to TPU transfers and avoids
+# compilation.
+#
+# Step 2
+# The TPU execution should be decomposed into subgraphs (4 at the moment):
+# 1. the main model
+# 2. selecting hidden states for each request
+# 3. sampler
+# 4. encoder.
+# Each subgraph should be decorated in a torch.compile. This is used to make
+# sure that we have the same subgraph topology in both dummy_run and
+# xecute_model. The results from these subgraphs should either be passed to
+# other subgraphs, or transferred from TPU to CPU using xla_tensor.cpu() for
+# subsequent processing on the CPU.
+#
+# Step 3
+# The dummy_run should be comprehensive, ensuring all potential input shapes and
+# branch predictions are included as subgraph inputs to facilitate
+# pre-compilation.
 class TPUModelRunner:
 
     def __init__(