diff --git a/python/sglang/multimodal_gen/runtime/models/dits/zimage.py b/python/sglang/multimodal_gen/runtime/models/dits/zimage.py
index b767b1e49..73f3061fc 100644
--- a/python/sglang/multimodal_gen/runtime/models/dits/zimage.py
+++ b/python/sglang/multimodal_gen/runtime/models/dits/zimage.py
@@ -8,7 +8,13 @@ from sglang.multimodal_gen.configs.models.dits.zimage import ZImageDitConfig
 from sglang.multimodal_gen.runtime.layers.activation import SiluAndMul
 from sglang.multimodal_gen.runtime.layers.attention import USPAttention
 from sglang.multimodal_gen.runtime.layers.layernorm import RMSNorm
-from sglang.multimodal_gen.runtime.layers.linear import ReplicatedLinear
+from sglang.multimodal_gen.runtime.layers.linear import (
+    ColumnParallelLinear,
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
 from sglang.multimodal_gen.runtime.layers.rotary_embedding import _apply_rotary_emb
 from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
 from sglang.multimodal_gen.runtime.platforms import current_platform
@@ -36,9 +42,13 @@ class TimestepEmbedder(nn.Module):
 
         self.mlp = nn.ModuleList(
             [
-                ReplicatedLinear(frequency_embedding_size, mid_size, bias=True),
+                ColumnParallelLinear(
+                    frequency_embedding_size, mid_size, bias=True, gather_output=False
+                ),
                 nn.SiLU(),
-                ReplicatedLinear(mid_size, out_size, bias=True),
+                RowParallelLinear(
+                    mid_size, out_size, bias=True, input_is_parallel=True
+                ),
             ]
         )
 
@@ -74,9 +84,11 @@ class TimestepEmbedder(nn.Module):
 class FeedForward(nn.Module):
     def __init__(self, dim: int, hidden_dim: int):
         super().__init__()
-        # Use ReplicatedLinear for gate and up projection (fused)
-        self.w13 = ReplicatedLinear(dim, hidden_dim * 2, bias=False)
-        self.w2 = ReplicatedLinear(hidden_dim, dim, bias=False)
+        # Use MergedColumnParallelLinear for gate and up projection (fused)
+        self.w13 = MergedColumnParallelLinear(
+            dim, [hidden_dim, hidden_dim], bias=False, gather_output=False
+        )
+        self.w2 = RowParallelLinear(hidden_dim, dim, bias=False, input_is_parallel=True)
         self.act = SiluAndMul()
 
     def forward(self, x):
@@ -97,14 +109,17 @@ class ZImageAttention(nn.Module):
     ) -> None:
         super().__init__()
         self.dim = dim
-        self.num_heads = num_heads
-        self.num_kv_heads = num_kv_heads
         self.head_dim = dim // num_heads
         self.qk_norm = qk_norm
 
-        # Use ReplicatedLinear for QKV projection (fused)
-        qkv_dim = dim + 2 * (num_kv_heads * self.head_dim)
-        self.to_qkv = ReplicatedLinear(dim, qkv_dim, bias=False)
+        # Use QKVParallelLinear for QKV projection (fused)
+        self.to_qkv = QKVParallelLinear(
+            hidden_size=dim,
+            head_size=self.head_dim,
+            total_num_heads=num_heads,
+            total_num_kv_heads=num_kv_heads,
+            bias=False,
+        )
 
         if self.qk_norm:
             self.norm_q = RMSNorm(self.head_dim, eps=eps)
@@ -113,7 +128,9 @@ class ZImageAttention(nn.Module):
             self.norm_q = None
             self.norm_k = None
 
-        self.to_out = nn.ModuleList([ReplicatedLinear(dim, dim, bias=False)])
+        self.to_out = nn.ModuleList(
+            [RowParallelLinear(dim, dim, bias=False, input_is_parallel=True)]
+        )
 
         self.attn = USPAttention(
             num_heads=num_heads,
@@ -130,12 +147,13 @@ class ZImageAttention(nn.Module):
         freqs_cis: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     ):
         qkv, _ = self.to_qkv(hidden_states)
-        kv_dim = self.head_dim * self.num_kv_heads
-        q, k, v = torch.split(qkv, [self.dim, kv_dim, kv_dim], dim=-1)
+        q_dim = self.to_qkv.num_heads * self.head_dim
+        kv_dim = self.to_qkv.num_kv_heads * self.head_dim
+        q, k, v = torch.split(qkv, [q_dim, kv_dim, kv_dim], dim=-1)
 
-        q = q.view(*q.shape[:-1], self.num_heads, self.head_dim)
-        k = k.view(*k.shape[:-1], self.num_kv_heads, self.head_dim)
-        v = v.view(*v.shape[:-1], self.num_kv_heads, self.head_dim)
+        q = q.view(*q.shape[:-1], self.to_qkv.num_heads, self.head_dim)
+        k = k.view(*k.shape[:-1], self.to_qkv.num_kv_heads, self.head_dim)
+        v = v.view(*v.shape[:-1], self.to_qkv.num_kv_heads, self.head_dim)
 
         if self.norm_q is not None:
             q = self.norm_q(q)
@@ -251,7 +269,9 @@ class FinalLayer(nn.Module):
     def __init__(self, hidden_size, out_channels):
         super().__init__()
         self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.linear = ReplicatedLinear(hidden_size, out_channels, bias=True)
+        self.linear = ColumnParallelLinear(
+            hidden_size, out_channels, bias=True, gather_output=True
+        )
 
         self.act = nn.SiLU()
         self.adaLN_modulation = nn.Sequential(
@@ -373,10 +393,11 @@ class ZImageTransformer2DModel(CachableDiT):
         for patch_idx, (patch_size, f_patch_size) in enumerate(
             zip(self.all_patch_size, self.all_f_patch_size)
         ):
-            x_embedder = ReplicatedLinear(
+            x_embedder = ColumnParallelLinear(
                 f_patch_size * patch_size * patch_size * self.in_channels,
                 self.dim,
                 bias=True,
+                gather_output=True,
             )
             all_x_embedder[f"{patch_size}-{f_patch_size}"] = x_embedder