diff --git a/modelopt/torch/quantization/model_calib.py b/modelopt/torch/quantization/model_calib.py
index ac20729da19..e8888b1789f 100644
--- a/modelopt/torch/quantization/model_calib.py
+++ b/modelopt/torch/quantization/model_calib.py
@@ -400,8 +400,8 @@ def max_calibrate(
         skip_forward_without_activation_calib: If True, skip the (potentially expensive)
             ``forward_loop`` when no enabled quantizer needs data-driven activation statistics
             (see :func:`_needs_activation_forward_for_max_calib`). Weight calibration still runs.
-            Opted into by the top-level ``max`` path and by GPTQ and imatrix, which run their own
-            forward; algorithms that always need activations (MSE, local Hessian, SmoothQuant,
+            Opted into by the top-level ``max`` path and by GPTQ, imatrix and local Hessian, which
+            run their own forward; algorithms that always need activations (MSE, SmoothQuant,
             SVDQuant) call this with the default False.
 
     See :class:`MaxCalibConfig <modelopt.torch.quantization.config.MaxCalibConfig>` for
@@ -1214,9 +1214,16 @@ def local_hessian_calibrate(
         warnings.warn("forward_loop must be provided for local_hessian; skipping local_hessian")
         return
 
-    # Phase 1: max-calibrate (also bootstraps dead experts + promotes/syncs NVFP4 static).
+    # Phase 1: max-calibrate (also bootstraps dead experts + promotes/syncs NVFP4 static). The
+    # Hessian pass below is local Hessian's own forward; max only runs one if activations need amax.
     print_rank_0("local_hessian: Running max calibration for all quantizers...")
-    max_calibrate(model, forward_loop, distributed_sync, shared_states=shared_states)
+    max_calibrate(
+        model,
+        forward_loop,
+        distributed_sync,
+        shared_states=shared_states,
+        skip_forward_without_activation_calib=True,
+    )
 
     names = module_name_maps(model)
 
diff --git a/tests/unit/torch/quantization/test_local_hessian.py b/tests/unit/torch/quantization/test_local_hessian.py
index d8b4c4eecec..52f53664a9a 100644
--- a/tests/unit/torch/quantization/test_local_hessian.py
+++ b/tests/unit/torch/quantization/test_local_hessian.py
@@ -132,6 +132,29 @@ def test_refines_amax_beyond_max_and_plain_mse(self):
         assert any(not torch.allclose(lh[n], max_amax[n]) for n in lh)  # refined past max-cal
         assert any(not torch.allclose(lh[n], mse[n]) for n in lh)  # Hessian changed the choice
 
+    @pytest.mark.parametrize(
+        ("input_cfg", "forwards"),
+        [(None, 1), ({"num_bits": 8, "axis": None}, 2)],
+        ids=["weight_only", "static_input_amax"],
+    )
+    def test_max_calibration_runs_a_forward_only_for_activation_amax(self, input_cfg, forwards):
+        """Local Hessian's own forward collects its Hessians, so max calibration adds one only
+        when an activation quantizer needs a data-driven amax."""
+        quant_cfg = {**INT8_WEIGHT_CFG, "quant_cfg": list(INT8_WEIGHT_CFG["quant_cfg"])}
+        if input_cfg is not None:
+            quant_cfg["quant_cfg"].append({"quantizer_name": "*input_quantizer", "cfg": input_cfg})
+        torch.manual_seed(0)
+        model = SimpleLinear()
+        mtq.quantize(model, quant_cfg, forward_loop=_make_forward_loop())
+        calls = []
+
+        def forward_loop(model):
+            calls.append(None)
+            _make_forward_loop()(model)
+
+        local_hessian_calibrate(model, forward_loop, fp8_scale_sweep=False)
+        assert len(calls) == forwards
+
     def test_warns_with_module_name_when_cin_not_divisible(self):
         class _OddModel(nn.Module):
             def __init__(self):