diff --git a/src/maxtext/layers/moe.py b/src/maxtext/layers/moe.py
index 5ea638f426..e649f86212 100644
--- a/src/maxtext/layers/moe.py
+++ b/src/maxtext/layers/moe.py
@@ -3363,12 +3363,23 @@ def fused_moe_matmul(
         self.config.decoder_block not in (ctypes.DecoderBlockType.LLAMA4, ctypes.DecoderBlockType.GEMMA4)
     )
 
-    output_2d = fused_moe_func(
+    # The fused kernel quantizes for itself: expert weights go in pre-quantized with
+    # per-block scales (when the qwix rule covers the grouped matmul) and the kernel
+    # quantizes the activations in-kernel, accumulating in f32. The call runs outside
+    # qwix's interception: qwix only guards pallas_call, while the kernel is launched
+    # through pl.kernel, so under a QtProvider its tiled matmuls would otherwise be
+    # fake-quantized into fp8 x fp8 with a bf16 accumulator, which Mosaic rejects.
+    rule = quantizations.get_fused_moe_rule()
+    quantized_w1, w1_scale = quantizations.quantize_weight_for_fused_moe(fused_kernel, rule)
+    quantized_w2, w2_scale = quantizations.quantize_weight_for_fused_moe(wo_kernel, rule)
+    fused_moe = quantizations.without_qwix_interception(fused_moe_func)
+
+    output_2d = fused_moe(
         hidden_states=hidden_states,
-        w1=fused_kernel,
-        w2=wo_kernel,
-        w1_scale=None,
-        w2_scale=None,
+        w1=quantized_w1,
+        w2=quantized_w2,
+        w1_scale=w1_scale,
+        w2_scale=w2_scale,
         w1_bias=None,
         w2_bias=None,
         gating_output=gating_output,
diff --git a/src/maxtext/layers/quantizations.py b/src/maxtext/layers/quantizations.py
index 0ef3aeca55..d604b98cb0 100644
--- a/src/maxtext/layers/quantizations.py
+++ b/src/maxtext/layers/quantizations.py
@@ -31,6 +31,7 @@
 from qwix._src.core import numerics
 from qwix._src.core import dot_general_qt
 from qwix._src.core import sparsity
+from qwix._src import interception as qwix_interception
 
 import jax
 import jax.numpy as jnp
@@ -66,6 +67,7 @@ def _safe_find_param(x, ptq_array_type=None):
 from maxtext.configs.types import TeCommGemmOverlapPolicy
 from maxtext.common.common_types import DType, Config
 from maxtext.inference.kvcache import KVQuant
+from maxtext.utils import max_logging
 
 # Params used to define mixed precision quantization configs
 DEFAULT = "__default__"  # default config
@@ -970,6 +972,74 @@ def maybe_quantize_model(model, config):
   return model
 
 
+# Quantized weight dtypes that tpu-inference's fused MoE kernel (gmm_v2) takes with
+# per-block scales and dequantizes in-kernel. Any other qtype keeps the expert weights
+# unquantized on the fused path.
+FUSED_MOE_KERNEL_WEIGHT_QTYPES = (jnp.dtype(jnp.float8_e4m3fn), jnp.dtype(jnp.int8))
+
+
+def get_fused_moe_rule() -> qwix.QuantizationRule | None:
+  """Returns the qwix rule that governs the fused MoE grouped matmul, if any.
+
+  The fused kernel is the same grouped matmul that MaxText's own megablox / ragged_dot
+  paths implement, so it takes its rule from the "gmm" op exactly like they do. Rules
+  that only list "dot_general" (the plain fp8 / int8 recipes) leave the experts
+  unquantized on every MoE path, including this one.
+  """
+  return qpl.get_current_rule("gmm")
+
+
+def quantize_weight_for_fused_moe(
+    kernel: jax.Array, rule: qwix.QuantizationRule | None
+) -> tuple[jax.Array, jax.Array | None]:
+  """Quantizes an [E, K, N] expert weight for tpu-inference's fused MoE kernel.
+
+  The kernel contracts over K, dequantizes after the matmul with scales laid out as
+  [E, num_blocks, 1, N] (blocks along K), and quantizes the activations itself with a
+  32-bit accumulator. Scale granularity follows the qwix rule: channelwise over (E, N),
+  plus blockwise along K when the rule sets tile_size.
+
+  Returns the (possibly unchanged) weight and its scale, or None when the rule does not
+  ask for a weight dtype the kernel supports.
+  """
+
+  weight_qtype = getattr(rule, "weight_qtype", None) if rule is not None else None
+  if weight_qtype is None:
+    return kernel, None
+  qtype = jnp.dtype(weight_qtype)
+  if qtype not in FUSED_MOE_KERNEL_WEIGHT_QTYPES:
+    max_logging.log(f"fused MoE kernel does not take {qtype} weights; keeping expert weights in {kernel.dtype}.")
+    return kernel, None
+  if kernel.ndim != 3:
+    raise ValueError(f"fused MoE expert weights must be [E, K, N], got {kernel.shape}")
+
+  tile_size = getattr(rule, "tile_size", None)
+  tiled_axes = {1: tile_size} if tile_size else {}
+  cal_method = getattr(rule, "weight_calibration_method", None)
+  quant_kwargs = {"calibration_method": cal_method} if cal_method is not None else {}
+
+  quantized = qpl.quantize(
+      kernel,
+      qtype,
+      channelwise_axes=(0, 2),
+      tiled_axes=tiled_axes,
+      scale_dtype=jnp.float32,
+      **quant_kwargs,
+  )
+  scale = jnp.expand_dims(quantized.scale, axis=2)
+  return quantized.qvalue, scale
+
+
+def without_qwix_interception(fn: Callable) -> Callable:
+  """Returns `fn` wrapped so that qwix's op interception is off while it runs.
+
+  For ops that do their own quantization (tpu-inference's fused MoE), the qwix rule is
+  applied once up front to their inputs and the op itself is opaque to qwix: neither
+  the routing math around the kernel nor anything traced inside it gets rewritten.
+  """
+  return qwix_interception.disable_interceptions(fn)
+
+
 def _cast_reduced_from(arr, reduced_arr):
   aval = jax.typeof(reduced_arr)
   # In shard map
diff --git a/tests/unit/fused_moe_qwix_test.py b/tests/unit/fused_moe_qwix_test.py
new file mode 100644
index 0000000000..776d256f89
--- /dev/null
+++ b/tests/unit/fused_moe_qwix_test.py
@@ -0,0 +1,264 @@
+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# pylint: disable=missing-class-docstring,missing-function-docstring,unbalanced-tuple-unpacking
+
+"""Tests for the qwix boundary around tpu-inference's fused MoE kernel."""
+
+import sys
+import types
+import unittest
+from unittest import mock
+
+from flax import nnx
+import jax
+import jax.numpy as jnp
+import numpy as np
+import qwix
+
+from maxtext.common import common_types as ctypes
+from maxtext.layers import moe
+from maxtext.layers import quantizations
+
+
+def _assert_error_within(actual, ref, bound):
+  """Every |actual - ref| stays under `bound`, which broadcasts against the arrays."""
+  err = np.abs(np.asarray(actual - ref))
+  np.testing.assert_array_less(err, np.broadcast_to(np.asarray(bound) + 1e-6, err.shape))
+
+
+def _fp8_rule(**kwargs):
+  return qwix.QtRule(
+      module_path=".*",
+      weight_qtype=jnp.float8_e4m3fn,
+      act_qtype=jnp.float8_e4m3fn,
+      op_names=("dot_general", "gmm", "ragged_dot"),
+      **kwargs,
+  )
+
+
+class QuantizeWeightForFusedMoeTest(unittest.TestCase):
+  """quantize_weight_for_fused_moe produces what the fused kernel takes."""
+
+  def setUp(self):
+    super().setUp()
+    self.num_experts, self.in_dim, self.out_dim = 4, 256, 128
+    self.kernel = jax.random.normal(jax.random.key(0), (self.num_experts, self.in_dim, self.out_dim), jnp.bfloat16)
+
+  def test_no_rule_keeps_weight(self):
+    weight, scale = quantizations.quantize_weight_for_fused_moe(self.kernel, None)
+    self.assertIs(weight, self.kernel)
+    self.assertIsNone(scale)
+
+  def test_rule_without_weight_qtype_keeps_weight(self):
+    rule = qwix.QtRule(module_path=".*", act_qtype=jnp.float8_e4m3fn)
+    weight, scale = quantizations.quantize_weight_for_fused_moe(self.kernel, rule)
+    self.assertIs(weight, self.kernel)
+    self.assertIsNone(scale)
+
+  def test_unsupported_qtype_keeps_weight(self):
+    rule = qwix.QtRule(module_path=".*", weight_qtype=jnp.float8_e5m2)
+    weight, scale = quantizations.quantize_weight_for_fused_moe(self.kernel, rule)
+    self.assertIs(weight, self.kernel)
+    self.assertIsNone(scale)
+
+  def test_fp8_channelwise(self):
+    weight, scale = quantizations.quantize_weight_for_fused_moe(self.kernel, _fp8_rule())
+    self.assertEqual(weight.dtype, jnp.float8_e4m3fn)
+    self.assertEqual(weight.shape, self.kernel.shape)
+    # one scale per (expert, output column), laid out as the kernel expects
+    self.assertEqual(scale.shape, (self.num_experts, 1, 1, self.out_dim))
+    self.assertEqual(scale.dtype, jnp.float32)
+    dequantized = weight.astype(jnp.float32) * scale[:, 0]
+    ref = self.kernel.astype(jnp.float32)
+    # fp8 e4m3 keeps 3 mantissa bits: the error is bounded by half an ulp of the column max
+    col_max = jnp.max(jnp.abs(ref), axis=1, keepdims=True)
+    _assert_error_within(dequantized, ref, col_max / 16)
+
+  def test_fp8_blockwise_from_tile_size(self):
+    weight, scale = quantizations.quantize_weight_for_fused_moe(self.kernel, _fp8_rule(tile_size=64))
+    self.assertEqual(weight.dtype, jnp.float8_e4m3fn)
+    self.assertEqual(scale.shape, (self.num_experts, self.in_dim // 64, 1, self.out_dim))
+    blocks = weight.astype(jnp.float32).reshape(self.num_experts, self.in_dim // 64, 64, self.out_dim)
+    dequantized = (blocks * scale).reshape(self.kernel.shape)
+    ref = self.kernel.astype(jnp.float32)
+    block_max = jnp.max(jnp.abs(ref.reshape(blocks.shape)), axis=2, keepdims=True)
+    _assert_error_within(dequantized.reshape(blocks.shape), ref.reshape(blocks.shape), block_max / 16)
+
+  def test_int8_channelwise(self):
+    rule = qwix.QtRule(module_path=".*", weight_qtype=jnp.int8, op_names=("gmm",))
+    weight, scale = quantizations.quantize_weight_for_fused_moe(self.kernel, rule)
+    self.assertEqual(weight.dtype, jnp.int8)
+    self.assertEqual(scale.shape, (self.num_experts, 1, 1, self.out_dim))
+    dequantized = weight.astype(jnp.float32) * scale[:, 0]
+    ref = self.kernel.astype(jnp.float32)
+    col_max = jnp.max(jnp.abs(ref), axis=1, keepdims=True)
+    _assert_error_within(dequantized, ref, col_max / 127)
+
+  def test_rejects_non_3d_weight(self):
+    with self.assertRaises(ValueError):
+      quantizations.quantize_weight_for_fused_moe(self.kernel[0], _fp8_rule())
+
+  def test_rule_with_missing_attributes(self):
+    """getattr fallback handles rules missing tile_size or calibration_method."""
+
+    class MinimalRule:
+      weight_qtype = jnp.float8_e4m3fn
+
+    weight, scale = quantizations.quantize_weight_for_fused_moe(self.kernel, MinimalRule())
+    self.assertEqual(weight.dtype, jnp.float8_e4m3fn)
+    self.assertEqual(scale.shape, (self.num_experts, 1, 1, self.out_dim))
+
+
+class WithoutQwixInterceptionTest(unittest.TestCase):
+  """The boundary hides the qwix rule (and its op rewriting) from whatever runs inside."""
+
+  def test_rule_visible_outside_and_hidden_inside(self):
+    seen = {}
+
+    class Layer(nnx.Module):
+
+      def __init__(self):
+        self.w = nnx.Param(jnp.ones((32, 64), jnp.bfloat16))
+
+      def __call__(self, x):
+        seen["outside"] = quantizations.get_fused_moe_rule()
+
+        def opaque(y):
+          seen["inside"] = quantizations.get_fused_moe_rule()
+          return jnp.dot(y, self.w[...], preferred_element_type=jnp.float32)
+
+        out = quantizations.without_qwix_interception(opaque)(x)
+        seen["after"] = quantizations.get_fused_moe_rule()
+        return out
+
+    x = jax.random.normal(jax.random.key(0), (8, 32), jnp.bfloat16)
+    layer = qwix.quantize_model(Layer(), qwix.QtProvider([_fp8_rule()]), x)
+    jaxpr = str(jax.make_jaxpr(layer)(x))
+
+    self.assertIsNotNone(seen["outside"])
+    self.assertIsNone(seen["inside"])
+    self.assertIsNotNone(seen["after"])
+    # the dot inside the boundary is left alone: no fp8 casts anywhere in the trace
+    self.assertNotIn("float8", jaxpr)
+
+
+class FusedMoeMatmulTest(unittest.TestCase):
+  """RoutedMoE.fused_moe_matmul hands the kernel pre-quantized weights, outside qwix."""
+
+  num_experts, top_k, emb_dim, mlp_dim, tokens = 4, 2, 64, 128, 16
+
+  def _fake_tpu_inference(self, calls):
+    """Stand-in for the tpu-inference modules fused_moe_matmul imports."""
+
+    def fused_moe_func(**kwargs):
+      calls.append(dict(kwargs, rule_inside=quantizations.get_fused_moe_rule()))
+      return jnp.zeros((kwargs["hidden_states"].shape[0], self.emb_dim), jnp.bfloat16)
+
+    envs = types.SimpleNamespace(
+        ENABLE_RS_KERNEL=False, USE_GMM_FUSED_RS_KERNEL=False, ONEHOT_MOE_PERMUTE_THRESHOLD=0, VLLM_MOE_CHUNK_SIZE=0
+    )
+    pkg = types.ModuleType("tpu_inference")
+    pkg.envs = envs
+    layers = types.ModuleType("tpu_inference.layers")
+    common = types.ModuleType("tpu_inference.layers.common")
+    gmm = types.ModuleType("tpu_inference.layers.common.fused_moe_gmm")
+    gmm.fused_moe_func = fused_moe_func
+    return {
+        "tpu_inference": pkg,
+        "tpu_inference.envs": envs,
+        "tpu_inference.layers": layers,
+        "tpu_inference.layers.common": common,
+        "tpu_inference.layers.common.fused_moe_gmm": gmm,
+    }
+
+  def _make_layer(self, calls):
+    test = self
+    config = types.SimpleNamespace(
+        mlp_activations=("silu",),
+        routed_score_func="softmax",
+        norm_topk_prob=True,
+        decoder_block=ctypes.DecoderBlockType.MIXTRAL,
+    )
+
+    class Layer(nnx.Module):
+      """Drives RoutedMoE.fused_moe_matmul with just the attributes it reads."""
+
+      def __init__(self):
+        keys = jax.random.split(jax.random.key(0), 3)
+        self.w0 = nnx.Param(jax.random.normal(keys[0], (test.num_experts, test.emb_dim, test.mlp_dim), jnp.bfloat16))
+        self.w1 = nnx.Param(jax.random.normal(keys[1], (test.num_experts, test.emb_dim, test.mlp_dim), jnp.bfloat16))
+        self.wo = nnx.Param(jax.random.normal(keys[2], (test.num_experts, test.mlp_dim, test.emb_dim), jnp.bfloat16))
+        self.config = config
+        self.num_experts = test.num_experts
+        self.num_experts_per_tok = test.top_k
+        self.mesh = None
+
+      def get_expert_parallelism_size(self):
+        return 1
+
+      def __call__(self, inputs, gate_logits):
+        with mock.patch.dict(sys.modules, test._fake_tpu_inference(calls)):
+          out, _, _ = moe.RoutedMoE.fused_moe_matmul(self, inputs, gate_logits, self.wo[...], self.w0[...], self.w1[...])
+        return out
+
+    return Layer()
+
+  def _inputs(self):
+    inputs = jax.random.normal(jax.random.key(1), (1, self.tokens, self.emb_dim), jnp.bfloat16)
+    gate_logits = jax.random.normal(jax.random.key(2), (1, self.tokens, self.num_experts), jnp.float32)
+    return inputs, gate_logits
+
+  def test_unquantized_without_qwix(self):
+    calls = []
+    inputs, gate_logits = self._inputs()
+    self._make_layer(calls)(inputs, gate_logits)
+    (call,) = calls
+    self.assertEqual(call["w1"].dtype, jnp.bfloat16)
+    self.assertEqual(call["w1"].shape, (self.num_experts, self.emb_dim, 2 * self.mlp_dim))
+    self.assertIsNone(call["w1_scale"])
+    self.assertIsNone(call["w2_scale"])
+
+  def test_fp8_rule_prequantizes_weights_outside_qwix(self):
+    calls = []
+    inputs, gate_logits = self._inputs()
+    layer = qwix.quantize_model(self._make_layer(calls), qwix.QtProvider([_fp8_rule()]), inputs, gate_logits)
+    calls.clear()
+    layer(inputs, gate_logits)
+    (call,) = calls
+    # weights arrive quantized, with scales in the kernel's [E, blocks, 1, N] layout
+    self.assertEqual(call["w1"].dtype, jnp.float8_e4m3fn)
+    self.assertEqual(call["w2"].dtype, jnp.float8_e4m3fn)
+    self.assertEqual(call["w1_scale"].shape, (self.num_experts, 1, 1, 2 * self.mlp_dim))
+    self.assertEqual(call["w2_scale"].shape, (self.num_experts, 1, 1, self.emb_dim))
+    # and the kernel itself runs outside qwix's interception
+    self.assertIsNone(call["rule_inside"])
+
+  def test_dot_general_only_rule_keeps_experts_unquantized(self):
+    calls = []
+    inputs, gate_logits = self._inputs()
+    rule = qwix.QtRule(
+        module_path=".*", weight_qtype=jnp.float8_e4m3fn, act_qtype=jnp.float8_e4m3fn, op_names=("dot_general",)
+    )
+    layer = qwix.quantize_model(self._make_layer(calls), qwix.QtProvider([rule]), inputs, gate_logits)
+    calls.clear()
+    layer(inputs, gate_logits)
+    (call,) = calls
+    self.assertEqual(call["w1"].dtype, jnp.bfloat16)
+    self.assertIsNone(call["w1_scale"])
+    self.assertIsNone(call["rule_inside"])
+
+
+if __name__ == "__main__":
+  unittest.main()