diff --git a/src/exo/worker/engines/mlx/auto_parallel.py b/src/exo/worker/engines/mlx/auto_parallel.py
index 2ec9900e..69ea2984 100644
--- a/src/exo/worker/engines/mlx/auto_parallel.py
+++ b/src/exo/worker/engines/mlx/auto_parallel.py
@@ -24,8 +24,6 @@ from mlx_lm.models.glm4_moe import Model as Glm4MoeModel
 from mlx_lm.models.glm4_moe import MoE
 from mlx_lm.models.glm4_moe_lite import Glm4MoeLiteDecoderLayer, Glm4MoeLiteMLP
 from mlx_lm.models.glm4_moe_lite import Model as GLM4MoeLiteModel
-from mlx_lm.models.glm_moe_dsa import Glm4MoeLiteMoE as GlmMoeDsaMoE
-from mlx_lm.models.glm_moe_dsa import Model as GlmMoeDsaModel
 from mlx_lm.models.gpt_oss import GptOssMoeModel
 from mlx_lm.models.gpt_oss import Model as GptOssModel
 from mlx_lm.models.kimi_k25 import Model as KimiK25Model
@@ -408,14 +406,6 @@ def tensor_auto_parallel(
             all_to_sharded_linear_in_place,
             sharded_to_all_linear_in_place,
         )
-    elif isinstance(model, GlmMoeDsaModel):
-        tensor_parallel_sharding_strategy = GlmMoeDsaShardingStrategy(
-            group,
-            all_to_sharded_linear,
-            sharded_to_all_linear,
-            all_to_sharded_linear_in_place,
-            sharded_to_all_linear_in_place,
-        )
     elif isinstance(model, Glm4MoeModel):
         tensor_parallel_sharding_strategy = Glm4MoeShardingStrategy(
             group,
@@ -667,62 +657,6 @@ class GLM4MoeLiteShardingStrategy(TensorParallelShardingStrategy):
         return model
 
 
-class GlmMoeDsaShardingStrategy(TensorParallelShardingStrategy):
-    def shard_model(
-        self,
-        model: nn.Module,
-        timeout_seconds: float,
-        on_timeout: TimeoutCallback | None,
-    ) -> nn.Module:
-        model = cast(GlmMoeDsaModel, model)
-        for layer in model.layers:
-            eval_with_timeout(
-                layer.parameters(),
-                timeout_seconds / len(model.layers),
-                on_timeout,
-            )
-            layer.self_attn.q_b_proj = self.all_to_sharded_linear(
-                layer.self_attn.q_b_proj
-            )
-            layer.self_attn.o_proj = self.sharded_to_all_linear(layer.self_attn.o_proj)
-            layer.self_attn.num_heads //= self.N
-
-            num_heads = layer.self_attn.num_heads
-            sh = self.group.rank() * num_heads
-            eh = sh + num_heads
-
-            def shard_heads(w: mx.array, sh: int = sh, eh: int = eh) -> mx.array:
-                return w[sh:eh]
-
-            layer.self_attn.embed_q.apply(shard_heads)
-            layer.self_attn.unembed_out.apply(shard_heads)
-
-            if isinstance(layer.mlp, Glm4MoeLiteMLP):
-                layer.mlp.gate_proj = self.all_to_sharded_linear(layer.mlp.gate_proj)
-                layer.mlp.down_proj = self.sharded_to_all_linear(layer.mlp.down_proj)
-                layer.mlp.up_proj = self.all_to_sharded_linear(layer.mlp.up_proj)
-            else:
-                moe = cast(GlmMoeDsaMoE, layer.mlp)
-                if moe.shared_experts is not None:
-                    self.all_to_sharded_linear_in_place(
-                        moe.shared_experts.gate_proj
-                    )
-                    self.sharded_to_all_linear_in_place(
-                        moe.shared_experts.down_proj
-                    )
-                    self.all_to_sharded_linear_in_place(
-                        moe.shared_experts.up_proj
-                    )
-                self.all_to_sharded_linear_in_place(moe.switch_mlp.gate_proj)
-                self.sharded_to_all_linear_in_place(moe.switch_mlp.down_proj)
-                self.all_to_sharded_linear_in_place(moe.switch_mlp.up_proj)
-                layer.mlp = ShardedMoE(moe)  # type: ignore
-                layer.mlp.sharding_group = self.group
-            mx.eval(layer)
-
-        return model
-
-
 class WrappedMiniMaxAttention(CustomMlxLayer):
     def __init__(self, layer: _LayerCallable, group: mx.distributed.Group):
         super().__init__(layer)