diff --git a/tensorrt_edgellm/checkpoint/repacking.py b/tensorrt_edgellm/checkpoint/repacking.py
index 5f2a34e10..0b2af2533 100644
--- a/tensorrt_edgellm/checkpoint/repacking.py
+++ b/tensorrt_edgellm/checkpoint/repacking.py
@@ -1320,7 +1320,8 @@ def repack_nvfp4_gated_moe_experts(
             :data:`NVFP4_MOE_INTERMEDIATE_SIZE_ALIGNMENT` for ``"concat"``.
     """
     from ..models.linear import \
-        is_nvfp4_linear  # local import to avoid circular dep
+        NVFP4A16MarlinLinear  # local import to avoid circular dep
+    from ..models.linear import is_nvfp4_linear
 
     if fc1_layout == "interleave":
         build_fc1_dense = _interleave_gated_moe_fc1
@@ -1364,8 +1365,11 @@ def repack_nvfp4_gated_moe_experts(
         gate = expert.gate_proj
         up = expert.up_proj
         down = expert.down_proj
-        if not (is_nvfp4_linear(gate) and is_nvfp4_linear(up)
-                and is_nvfp4_linear(down)):
+
+        def _is_nvfp4(m):
+            return is_nvfp4_linear(m) or isinstance(m, NVFP4A16MarlinLinear)
+
+        if not (_is_nvfp4(gate) and _is_nvfp4(up) and _is_nvfp4(down)):
             raise TypeError("Gated NVFP4 MoE experts must use NVFP4 quant")
 
         gate_dense = decode_modelopt_nvfp4(gate.weight, gate.weight_scale,
diff --git a/tensorrt_edgellm/models/qwen3_moe/modeling_qwen3_moe.py b/tensorrt_edgellm/models/qwen3_moe/modeling_qwen3_moe.py
index 42190b411..f3de6fcfb 100644
--- a/tensorrt_edgellm/models/qwen3_moe/modeling_qwen3_moe.py
+++ b/tensorrt_edgellm/models/qwen3_moe/modeling_qwen3_moe.py
@@ -64,7 +64,7 @@
 import torch
 import torch.nn as nn
 
-from ...config import QUANT_FP16, QUANT_NVFP4, ModelConfig
+from ...config import QUANT_FP16, QUANT_NVFP4, QUANT_NVFP4_A16, ModelConfig
 from ..default.modeling_default import (MLP, Attention, OnnxSpec, RMSNorm,
                                         _make_flat_wrapper)
 from ..linear import FP16Linear, make_linear
@@ -223,7 +223,8 @@ def __init__(self, config: ModelConfig, module_prefix: str = "") -> None:
         self.hidden_size = config.hidden_size
         self.group_size = config.quant.group_size
         self.zero_point_offset = config.quant.gptq_zero_point_offset
-        self._use_nvfp4_moe = config.quant.quant_type == QUANT_NVFP4
+        self._use_nvfp4_moe = config.quant.quant_type in (QUANT_NVFP4,
+                                                          QUANT_NVFP4_A16)
         self._use_fp16_moe = config.quant.quant_type == QUANT_FP16
         # All paths compute the same SwiGLU expert FFN; the integer is just
         # each plugin's own enum for it. Int4MoePlugin names the elementwise