diff --git a/modelopt/torch/quantization/qtensor/nvfp4_tensor.py b/modelopt/torch/quantization/qtensor/nvfp4_tensor.py
index fe30e283c2d..bb39c8a81e3 100644
--- a/modelopt/torch/quantization/qtensor/nvfp4_tensor.py
+++ b/modelopt/torch/quantization/qtensor/nvfp4_tensor.py
@@ -122,10 +122,16 @@ def get_weights_scaling_factor_from_quantizer(
             expected_shape = (*weight.shape[:-1], num_blocks_per_row)
             per_block_scale = per_block_scale.view(expected_shape)
 
-            # Quantize scales to FP8
+            # Quantize scales to FP8. Saturate to the fp8_e4m3fn max (448) before the
+            # cast: when the [==0]=1.0 safety net above fires (per_block_amax was zero
+            # for an all-zero weight block) and global_amax is small, the pre-cast value
+            # explodes to ``1.0 * 448 / (global_amax/6)``. fp8_e4m3fn has no Inf, so any
+            # value >= 480 casts to NaN — clamp first to keep the stored byte finite.
             if not keep_high_precision:
-                per_block_scale = (per_block_scale * 448.0 / per_block_scale_max).to(
-                    torch.float8_e4m3fn
+                per_block_scale = (
+                    (per_block_scale * 448.0 / per_block_scale_max)
+                    .clamp_(max=448.0)
+                    .to(torch.float8_e4m3fn)
                 )
             return per_block_scale, weights_scaling_factor_2
         else:
diff --git a/tests/gpu/torch/quantization/test_nvfp4_static_quantizer_cuda.py b/tests/gpu/torch/quantization/test_nvfp4_static_quantizer_cuda.py
index b1b3691a797..430b7ee4113 100644
--- a/tests/gpu/torch/quantization/test_nvfp4_static_quantizer_cuda.py
+++ b/tests/gpu/torch/quantization/test_nvfp4_static_quantizer_cuda.py
@@ -21,6 +21,7 @@
 from modelopt.torch.quantization.calib import NVFP4MSECalibrator
 from modelopt.torch.quantization.config import QuantizerAttributeConfig
 from modelopt.torch.quantization.nn import NVFP4StaticQuantizer, TensorQuantizer
+from modelopt.torch.quantization.qtensor import NVFP4QTensor
 from modelopt.torch.quantization.tensor_quant import (
     scaled_e4m3_impl,
     static_blockwise_fp4_fake_quant,
@@ -64,6 +65,51 @@ def test_global_amax_property(self, device):
         quantizer.global_amax = None
         assert quantizer.global_amax is None
 
+    def test_export_fp8_scale_no_nan_for_zero_amax_block(self, device):
+        """Regression: export must not emit fp8 NaN bytes for an all-zero block.
+
+        When max-only calibration leaves ``_amax = 0`` for a fully-zero weight block,
+        the export's ``[per_block_scale == 0] = 1.0`` safety net drives the pre-cast
+        value to ``1.0 * 448 / (global_amax / 6)``. fp8_e4m3fn has no Inf, so any
+        pre-cast value >= 480 rounds to NaN — without a saturation clamp this writes
+        a 0x7F byte into ``weight_scale``. Reproduces the NaN seen in the saved
+        Kimi-K2.6-NVFP4-MSE checkpoint at expert 21 down_proj.
+        """
+        block_size = 16
+        cfg = QuantizerAttributeConfig(
+            num_bits=(2, 1),
+            block_sizes={-1: block_size, "type": "static", "scale_bits": (4, 3)},
+        )
+        quantizer = NVFP4StaticQuantizer(quant_attribute_cfg=cfg).to(device)
+
+        # Two-block weight: block 0 is non-trivial; block 1 is all zeros so its
+        # per-block amax is exactly 0.
+        weight = torch.zeros(1, 2 * block_size, device=device, dtype=torch.bfloat16)
+        weight[0, :block_size] = 0.1
+
+        per_block_amax = weight.abs().reshape(1, 2, block_size).amax(dim=-1).flatten()
+        quantizer.amax = per_block_amax
+        quantizer.global_amax = per_block_amax.max()
+
+        # Sanity: the bug only fires when the would-be cast value exceeds 480.
+        # With global_amax = 0.1, scale_in_fp8 for a zero block is
+        # 1.0 * 448 / (0.1 / 6) ≈ 26880 — well past the 480 NaN threshold.
+        assert (per_block_amax == 0).any()
+        assert quantizer.global_amax.float().item() < 1.0
+
+        weight_scale, _ = NVFP4QTensor.get_weights_scaling_factor_from_quantizer(
+            quantizer, weight, weights_scaling_factor_2=None
+        )
+        assert weight_scale.dtype == torch.float8_e4m3fn
+
+        # No fp8_e4m3fn NaN bytes (NaN encoding is (b & 0x7F) == 0x7F).
+        raw = weight_scale.view(torch.uint8)
+        n_nan = ((raw & 0x7F) == 0x7F).sum().item()
+        assert n_nan == 0, f"fp8 weight_scale contains {n_nan} NaN byte(s)"
+
+        # The all-zero block's stored fp8 scale should saturate to 448 (max finite).
+        assert raw.flatten()[1].item() == 0x7E
+
     def test_fake_quantize_with_both_amaxs(self, device):
         """Test _fake_quantize uses both _amax and _global_amax."""
         num_blocks = 4