diff --git a/python/sglang/srt/layers/attention/hybrid_linear_attn_backend.py b/python/sglang/srt/layers/attention/hybrid_linear_attn_backend.py
index 765224008..e0ddbcd05 100644
--- a/python/sglang/srt/layers/attention/hybrid_linear_attn_backend.py
+++ b/python/sglang/srt/layers/attention/hybrid_linear_attn_backend.py
@@ -17,11 +17,7 @@ from sglang.srt.layers.attention.fla.fused_recurrent import (
 from sglang.srt.layers.attention.fla.fused_sigmoid_gating_recurrent import (
     fused_sigmoid_gating_delta_rule_update,
 )
-from sglang.srt.layers.attention.fla.kda import (
-    chunk_kda,
-    fused_kda_gate,
-    fused_recurrent_kda,
-)
+from sglang.srt.layers.attention.fla.kda import chunk_kda, fused_recurrent_kda
 from sglang.srt.layers.attention.mamba.causal_conv1d_triton import (
     PAD_SLOT_ID,
     causal_conv1d_fn,
@@ -646,14 +642,10 @@ class KimiLinearAttnBackend(MambaAttnBackendBase):
         k_conv_bias = kwargs["k_conv_bias"]
         v_conv_bias = kwargs["v_conv_bias"]
 
-        A_log = kwargs["A_log"]
-        dt_bias = kwargs["dt_bias"]
-        b_proj = kwargs["b_proj"]
-        f_a_proj = kwargs["f_a_proj"]
-        f_b_proj = kwargs["f_b_proj"]
-        hidden_states = kwargs["hidden_states"]
         head_dim = kwargs["head_dim"]
         layer_id = kwargs["layer_id"]
+        beta = kwargs["beta"]
+        g = kwargs["gate"]
 
         layer_cache = self.req_to_token_pool.mamba2_layer_cache(layer_id)
         q_conv_state, k_conv_state, v_conv_state = layer_cache.conv
@@ -694,14 +686,6 @@ class KimiLinearAttnBackend(MambaAttnBackendBase):
             lambda x: rearrange(x, "n (h d) -> 1 n h d", d=head_dim), (q, k, v)
         )
 
-        beta = b_proj(hidden_states)[0].float().sigmoid()
-
-        g = f_b_proj(f_a_proj(hidden_states)[0])[0]
-        g = fused_kda_gate(g, A_log, head_dim, g_bias=dt_bias)
-
-        beta = beta.unsqueeze(0)
-        g = g.unsqueeze(0)
-
         initial_state = ssm_states[cache_indices].contiguous()
         (
             core_attn_out,
@@ -744,14 +728,10 @@ class KimiLinearAttnBackend(MambaAttnBackendBase):
         k_conv_bias = kwargs["k_conv_bias"]
         v_conv_bias = kwargs["v_conv_bias"]
 
-        A_log = kwargs["A_log"]
-        dt_bias = kwargs["dt_bias"]
-        b_proj = kwargs["b_proj"]
-        f_a_proj = kwargs["f_a_proj"]
-        f_b_proj = kwargs["f_b_proj"]
-        hidden_states = kwargs["hidden_states"]
         head_dim = kwargs["head_dim"]
         layer_id = kwargs["layer_id"]
+        beta = kwargs["beta"]
+        g = kwargs["gate"]
 
         query_start_loc = self.forward_metadata.query_start_loc
         cache_indices = self.forward_metadata.mamba_cache_indices
@@ -811,14 +791,6 @@ class KimiLinearAttnBackend(MambaAttnBackendBase):
             lambda x: rearrange(x, "n (h d) -> 1 n h d", d=head_dim), (q, k, v)
         )
 
-        beta = b_proj(hidden_states)[0].float().sigmoid()
-
-        g = f_b_proj(f_a_proj(hidden_states)[0])[0]
-        g = fused_kda_gate(g, A_log, head_dim, g_bias=dt_bias)
-
-        beta = beta.unsqueeze(0)
-        g = g.unsqueeze(0)
-
         core_attn_out = chunk_kda(
             q=q,
             k=k,
diff --git a/python/sglang/srt/models/kimi_linear.py b/python/sglang/srt/models/kimi_linear.py
index 5d93cb735..8a9029ce4 100644
--- a/python/sglang/srt/models/kimi_linear.py
+++ b/python/sglang/srt/models/kimi_linear.py
@@ -15,7 +15,7 @@ from sglang.srt.distributed import (
     tensor_model_parallel_all_reduce,
 )
 from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
-from sglang.srt.layers.attention.fla.kda import FusedRMSNormGated
+from sglang.srt.layers.attention.fla.kda import FusedRMSNormGated, fused_kda_gate
 from sglang.srt.layers.layernorm import RMSNorm
 from sglang.srt.layers.linear import (
     ColumnParallelLinear,
@@ -314,6 +314,14 @@ class KimiDeltaAttention(nn.Module):
             self.v_conv1d.weight.size(0), self.v_conv1d.weight.size(2)
         )
 
+        beta = self.b_proj(hidden_states)[0].float().sigmoid()
+        forget_gate = self.f_b_proj(self.f_a_proj(hidden_states)[0])[0]
+        forget_gate = fused_kda_gate(
+            forget_gate, self.A_log, self.head_dim, g_bias=self.dt_bias
+        )
+        beta = beta.unsqueeze(0)
+        forget_gate = forget_gate.unsqueeze(0)
+
         kwargs = {
             "q_proj_states": q_proj_states,
             "k_proj_states": k_proj_states,
@@ -324,14 +332,10 @@ class KimiDeltaAttention(nn.Module):
             "q_conv_bias": self.q_conv1d.bias,
             "k_conv_bias": self.k_conv1d.bias,
             "v_conv_bias": self.v_conv1d.bias,
-            "dt_bias": self.dt_bias,
-            "b_proj": self.b_proj,
-            "f_a_proj": self.f_a_proj,
-            "f_b_proj": self.f_b_proj,
-            "A_log": self.A_log,
             "head_dim": self.head_dim,
-            "hidden_states": hidden_states,
             "layer_id": self.layer_idx,
+            "beta": beta,
+            "gate": forget_gate,
         }
 
         core_attn_out = forward_batch.attn_backend.forward(
@@ -344,8 +348,8 @@ class KimiDeltaAttention(nn.Module):
         )
 
         g_proj_states = self.g_b_proj(self.g_a_proj(hidden_states)[0])[0]
-        g = rearrange(g_proj_states, "... (h d) -> ... h d", d=self.head_dim)
-        core_attn_out = self.o_norm(core_attn_out, g)
+        norm_gate = rearrange(g_proj_states, "... (h d) -> ... h d", d=self.head_dim)
+        core_attn_out = self.o_norm(core_attn_out, norm_gate)
         core_attn_out = rearrange(core_attn_out, "1 n h d -> n (h d)")
 
         return self.o_proj(core_attn_out)[0]