diff --git a/src/maxtext/configs/types.py b/src/maxtext/configs/types.py
index c35274cd24..ff686068dd 100644
--- a/src/maxtext/configs/types.py
+++ b/src/maxtext/configs/types.py
@@ -563,6 +563,7 @@ class Attention(BaseModel):
       False,
       description="Whether to use the Tokamax library for GMM kernel implementation.",
   )
+  tokamax_gmm_autotune: bool = Field(False, description="Whether to use tokamax auto-tuner for GMM.")
   ragged_block_size: int = Field(256, description="Block size for ragged attention.")
   enable_padding_causal_mask: bool = Field(True, description="Temporary flag for TE padding.")
   use_tokamax_splash: bool = Field(False, description="Whether to use tokamax splash attention.")
diff --git a/src/maxtext/layers/moe.py b/src/maxtext/layers/moe.py
index e23c3eba9f..c4d576aefe 100644
--- a/src/maxtext/layers/moe.py
+++ b/src/maxtext/layers/moe.py
@@ -19,6 +19,7 @@
 import functools
 import math
 import random
+import os
 from typing import Iterable, Optional, Tuple, Union
 
 from aqt.jax.v2 import aqt_tensor as aqt
@@ -43,6 +44,14 @@
 from qwix.contrib.sparsity import sparsity_module
 import qwix.pallas as qpl
 import tokamax
+from tokamax import config as tokamax_config
+from tokamax._src.ops.ragged_dot.pallas_mosaic_tpu import PallasMosaicTpuRaggedDot, Config
+from tokamax._src.autotuning.autotuner import AutotuningData
+from tokamax._src.benchmarking import BenchmarkData
+from tokamax._src.autotuning.api import AutotuningResult
+import immutabledict
+from tokamax._src.ops.ragged_dot import base
+import functools
 
 set_xla_metadata = xla_metadata.set_xla_metadata
 
@@ -1080,9 +1089,10 @@ def get_tokamax_group_sizes(group_sizes, inputs, kernel):
       elif self.config.attention == "vllm_rpa":
         return group_sizes
       else:
+        ep = self.get_expert_parallelism_size()
         return tokamax.RaggedDotGroupSizes(
             group_sizes,
-            (inputs.shape[0] // kernel.shape[0],) * kernel.shape[0],
+            (inputs.shape[0] // kernel.shape[0] // ep,) * kernel.shape[0],
         )
 
     def get_quantization_dtypes():
@@ -1121,14 +1131,47 @@ def gmm(inputs, kernel, tiling, group_sizes, expert_assignments, weight_gather_a
               weight_gather_axes=weight_gather_axes,
           )
         else:  # tokamax (unquantized)
-          output = tokamax.ragged_dot(
-              lhs=inputs,
-              rhs=kernel,
-              group_sizes=tokamax_group_sizes,
-              precision=jax.lax.Precision.DEFAULT,
-              preferred_element_type=self.dtype,
-              implementation="mosaic",
-          )
+          if self.config.tokamax_gmm_autotune:
+            # 1. Create configs from flags for backward pass
+            dlhs_config = Config(
+                tile_m=tiling[3],
+                tile_k=tiling[4],
+                tile_n=tiling[5],
+            )
+            drhs_config = Config(
+                tile_m=tiling[6],
+                tile_k=tiling[7],
+                tile_n=tiling[8],
+            )
+
+            # 2. Create custom ops for backward pass
+            dlhs_op = PallasMosaicTpuRaggedDot(config=dlhs_config)
+            drhs_op = PallasMosaicTpuRaggedDot(config=drhs_config)
+
+            # 3. Create custom vjp function
+            custom_vjp = functools.partial(base.vjp, dlhs_ragged_dot=dlhs_op, drhs_ragged_dot=drhs_op)
+
+            # 4. Create forward op with custom vjp and tiling from flags
+            fwd_config = Config(tile_m=tiling[0], tile_k=tiling[1], tile_n=tiling[2])
+            fwd_op = PallasMosaicTpuRaggedDot(config=fwd_config, vjp=custom_vjp)
+
+            output = tokamax.ragged_dot(
+                lhs=inputs,
+                rhs=kernel,
+                group_sizes=tokamax_group_sizes,
+                precision=jax.lax.Precision.DEFAULT,
+                preferred_element_type=self.dtype,
+                implementation=(fwd_op,),  # Pass the configured op directly!
+            )
+          else:
+            output = tokamax.ragged_dot(
+                lhs=inputs,
+                rhs=kernel,
+                group_sizes=tokamax_group_sizes,
+                precision=jax.lax.Precision.DEFAULT,
+                preferred_element_type=self.dtype,
+                implementation="mosaic",
+            )
       elif self.config.megablox:  # Older forked megablox
         output = mblx.gmm(
             lhs=inputs,
diff --git a/src/maxtext/models/deepseek_batchsplit_fp8.py b/src/maxtext/models/deepseek_batchsplit_fp8.py
index cef7c0646f..ee32fba127 100644
--- a/src/maxtext/models/deepseek_batchsplit_fp8.py
+++ b/src/maxtext/models/deepseek_batchsplit_fp8.py
@@ -29,6 +29,8 @@
 from maxtext.layers import quantizations
 import qwix.pallas as qpl
 import tokamax
+from tokamax import config as tokamax_config
+from tokamax._src.ops.ragged_dot.pallas_mosaic_tpu import PallasMosaicTpuRaggedDot, Config
 
 
 @functools.partial(
@@ -962,14 +964,25 @@ def gmm(
           qwix_rule=quantizations.get_fp8_full_qwix_rule_w_sparsity(config)[0],
       )
     else:
-      output = tokamax.ragged_dot(
-          lhs=inputs,
-          rhs=kernel,
-          group_sizes=tokamax.RaggedDotGroupSizes(group_sizes, len(inputs)),
-          precision=jax.lax.Precision.DEFAULT,
-          preferred_element_type=preferred_element_type,
-          implementation="mosaic",
-      )
+      if config.tokamax_gmm_autotune:
+        with tokamax_config.autotuning_cache_miss_fallback("autotune"):
+          output = tokamax.ragged_dot(
+              lhs=inputs,
+              rhs=kernel,
+              group_sizes=tokamax.RaggedDotGroupSizes(group_sizes, len(inputs)),
+              precision=jax.lax.Precision.DEFAULT,
+              preferred_element_type=preferred_element_type,
+              implementation="mosaic",
+          )
+      else:
+        output = tokamax.ragged_dot(
+            lhs=inputs,
+            rhs=kernel,
+            group_sizes=tokamax.RaggedDotGroupSizes(group_sizes, len(inputs)),
+            precision=jax.lax.Precision.DEFAULT,
+            preferred_element_type=preferred_element_type,
+            implementation="mosaic",
+        )
     return output
 
   gmm_fn = functools.partial(gmm, group_sizes=group_sizes, preferred_element_type=config.dtype)