diff --git a/sharktank/sharktank/types/sharding.py b/sharktank/sharktank/types/sharding.py
index ff925ec9c..81d2f31a5 100644
--- a/sharktank/sharktank/types/sharding.py
+++ b/sharktank/sharktank/types/sharding.py
@@ -143,48 +143,43 @@ def theta_sharding(self) -> ThetaSharding:
         )
 
 
-class LinearSplitParallelWeightAndBiasSharding(ThetaLayerSharding):
-    def __init__(self, shard_count: int, weight_and_bias_spit_dim: int = 0):
-        """Split one parallel dimension for both the weight and bias.
-        Since the weight is transposed before multiplying, the weight parallel
-        dimension is the same as the output(bias) dimension."""
+class LinearLayerSharding(ThetaLayerSharding):
+    def __init__(
+        self, premul_input: TensorSharding, weight: TensorSharding, bias: TensorSharding
+    ):
         super().__init__()
-        self.shard_count = shard_count
-        self.weight_and_bias_spit_dim = weight_and_bias_spit_dim
+        self.premul_input = premul_input
+        self.weight = weight
+        self.bias = bias
 
     def theta_sharding(self) -> ThetaSharding:
         return ThetaSharding(
             {
-                "premul_input": Replicated(shard_count=self.shard_count),
-                "weight": Split(
-                    shard_count=self.shard_count,
-                    shard_dim=self.weight_and_bias_spit_dim,
-                ),
-                "bias": Split(
-                    shard_count=self.shard_count,
-                    shard_dim=self.weight_and_bias_spit_dim,
-                ),
+                "premul_input": self.premul_input,
+                "weight": self.weight,
+                "bias": self.bias,
             }
         )
 
 
-class LinearSplitReductionDimSharding(ThetaLayerSharding):
-    def __init__(self, shard_count: int):
-        super().__init__()
-        self.shard_count = shard_count
+class LinearSplitParallelWeightAndBiasSharding(LinearLayerSharding):
+    def __init__(self, shard_count: int, weight_and_bias_spit_dim: int = 0):
+        """Split one parallel dimension for both the weight and bias.
+        Since the weight is transposed before multiplying, the weight parallel
+        dimension is the same as the output(bias) dimension."""
+        super().__init__(
+            premul_input=Replicated(shard_count=shard_count),
+            weight=Split(shard_count=shard_count, shard_dim=weight_and_bias_spit_dim),
+            bias=Split(shard_count=shard_count, shard_dim=weight_and_bias_spit_dim),
+        )
 
-    def theta_sharding(self) -> ThetaSharding:
-        return ThetaSharding(
-            {
-                "premul_input": Replicated(shard_count=self.shard_count),
-                "weight": Split(
-                    shard_count=self.shard_count,
-                    shard_dim=1,
-                ),
-                "bias": Replicated(
-                    shard_count=self.shard_count,
-                ),
-            }
+
+class LinearSplitReductionDimSharding(LinearLayerSharding):
+    def __init__(self, shard_count: int):
+        super().__init__(
+            premul_input=Replicated(shard_count=shard_count),
+            weight=Split(shard_count=shard_count, shard_dim=1),
+            bias=Replicated(shard_count=shard_count),
         )