diff --git a/docs/how_to/tutorials/e2e_opt_model.py b/docs/how_to/tutorials/e2e_opt_model.py
index 88cc86bfa800..e0e8cfc4e8c7 100644
--- a/docs/how_to/tutorials/e2e_opt_model.py
+++ b/docs/how_to/tutorials/e2e_opt_model.py
@@ -95,13 +95,32 @@
 # leverage MetaSchedule to tune the model and store the tuning logs to the database. We also
 # apply the database to the model to get the best performance.
 #
-
-TOTAL_TRIALS = 8000  # Change to 20000 for better performance if needed
+# The ResNet18 model will be divided into 20 independent tuning tasks during compilation.
+# To ensure each task receives adequate tuning resources in one iteration while providing
+# early feedback:
+#
+# - To quickly observe tuning progress, each task is allocated a maximum of 16 trials per
+#   iteration (controlled by ``max_trials_per_task=16``). Setting ``total_trials`` to at least
+#   ``320 (20 tasks * 16 trials)`` ensures every task receives one full iteration of tuning.
+# - If ``max_trials_per_task`` is unspecified, the system defaults to ``min(max_trials_per_iter=64,
+#   total_trials)`` trials per task per iteration. This may lead to undersubscribed tuning when
+#   ``total_trials`` is insufficient (e.g., ``64 < total_trials < 20 * 64``), potentially skipping
+#   some tasks entirely, leaving critical operators unoptimized or missing thread binding for
+#   untuned tasks. Explicitly setting both parameters avoids this issue and provides deterministic
+#   resource allocation across all tasks.
+
+TOTAL_TRIALS = 320  # Change to 20000 for better performance if needed
 target = tvm.target.Target("nvidia/geforce-rtx-3090-ti")  # Change to your target device
 work_dir = "tuning_logs"
 
 if not IS_IN_CI:
-    mod = relax.get_pipeline("static_shape_tuning", target=target, total_trials=TOTAL_TRIALS)(mod)
+    mod = relax.get_pipeline(
+        "static_shape_tuning",
+        target=target,
+        work_dir=work_dir,
+        total_trials=TOTAL_TRIALS,
+        max_trials_per_task=16,
+    )(mod)
 
     # Only show the main function
     mod["main"].show()
@@ -119,6 +138,6 @@
     # Need to allocate data and params on GPU device
     gpu_data = tvm.nd.array(np.random.rand(1, 3, 224, 224).astype("float32"), dev)
     gpu_params = [tvm.nd.array(p, dev) for p in params["main"]]
-    gpu_out = vm["main"](gpu_data, *gpu_params).numpy()
+    gpu_out = vm["main"](gpu_data, *gpu_params)[0].numpy()
 
     print(gpu_out.shape)
diff --git a/python/tvm/relax/pipeline.py b/python/tvm/relax/pipeline.py
index 37ef6156e4e7..706dd72c2760 100644
--- a/python/tvm/relax/pipeline.py
+++ b/python/tvm/relax/pipeline.py
@@ -21,7 +21,7 @@
 as it is or serves as a basis to do further composition.
 """
 # pylint: disable=unused-argument
-from typing import Union
+from typing import Union, Optional
 
 import tvm
 from tvm import meta_schedule as ms
@@ -111,6 +111,7 @@ def static_shape_tuning_pipeline(
     target: Union[str, tvm.target.Target],
     work_dir: str = "tuning_logs",
     cpu_weight_prepack: bool = False,
+    max_trials_per_task: Optional[int] = None,
 ):
     """Tune the static shape model and store the log to database.
 
@@ -128,6 +129,9 @@ def static_shape_tuning_pipeline(
     cpu_weight_prepack : bool
         Whether to enable the cpu weight prepack feature.
 
+    max_trials_per_task : Optional[int]
+        The maximum number of trials to run per task.
+
     Note
     ----
     `cpu_weight_prepack` is expected to be `True` when running on CPU for
@@ -142,6 +146,7 @@ def static_shape_tuning_pipeline(
             target="llvm -num-cores 16",
             work_dir="tuning_logs",
             cpu_weight_prepack=True,
+            max_trials_per_task=64,
         )(mod)
 
         ex = tvm.compile(mod, target=target)
@@ -177,7 +182,12 @@ def _pipeline(mod: tvm.ir.IRModule, _ctx: tvm.transform.PassContext) -> tvm.ir.I
                     *pre_tuning_layout_rewrite,
                     # Skip tuning if total_trials is 0
                     (
-                        transform.MetaScheduleTuneIRMod({}, work_dir, total_trials)
+                        transform.MetaScheduleTuneIRMod(
+                            params={},
+                            work_dir=work_dir,
+                            max_trials_global=total_trials,
+                            max_trials_per_task=max_trials_per_task,
+                        )
                         if total_trials > 0
                         else tvm.transform.Sequential([])
                     ),