diff --git a/vllm/model_executor/models/qwen3_next.py b/vllm/model_executor/models/qwen3_next.py
index f452ba871582..34b62125151c 100644
--- a/vllm/model_executor/models/qwen3_next.py
+++ b/vllm/model_executor/models/qwen3_next.py
@@ -551,10 +551,7 @@ def _forward(
             mixed_qkv_non_spec
         )
 
-        beta = b.sigmoid()
-        # g = -self.A_log.float().exp() * F.softplus(a.float() + self.dt_bias)
-        g = fused_gdn_gating(self.A_log, a, self.dt_bias)
-        g, beta = map(lambda x: rearrange(x, "l d -> 1 l d"), (g, beta))
+        g, beta = fused_gdn_gating(self.A_log, a, b, self.dt_bias)
 
         if spec_sequence_masks is not None:
             if attn_metadata.num_prefills == 0 and attn_metadata.num_decodes == 0:
@@ -1296,12 +1293,13 @@ def gdn_attention_fake(
 )
 
 
-# g = -self.A_log.float().exp() * F.softplus(a.float() + self.dt_bias)
 @triton.jit
 def fused_gdn_gating_kernel(
     g,
+    beta_output,
     A_log,
     a,
+    b,
     dt_bias,
     seq_len,
     NUM_HEADS: tl.constexpr,
@@ -1315,6 +1313,7 @@ def fused_gdn_gating_kernel(
     mask = head_off < NUM_HEADS
     blk_A_log = tl.load(A_log + head_off, mask=mask)
     blk_a = tl.load(a + off, mask=mask)
+    blk_b = tl.load(b + off, mask=mask)
     blk_bias = tl.load(dt_bias + head_off, mask=mask)
     # If the model is loaded in fp16, without the .float() here, A might be -inf
     x = blk_a.to(tl.float32) + blk_bias.to(tl.float32)
@@ -1323,20 +1322,42 @@ def fused_gdn_gating_kernel(
     )
     blk_g = -tl.exp(blk_A_log.to(tl.float32)) * softplus_x
     tl.store(g + off, blk_g.to(g.dtype.element_ty), mask=mask)
+    # compute beta_output = sigmoid(b)
+    blk_beta = 1.0 / (1.0 + tl.exp(-blk_b.to(tl.float32)))
+    tl.store(beta_output + off, blk_beta.to(beta_output.dtype.element_ty), mask=mask)
 
 
 def fused_gdn_gating(
     A_log: torch.Tensor,
     a: torch.Tensor,
+    b: torch.Tensor,
     dt_bias: torch.Tensor,
     beta: float = 1.0,
     threshold: float = 20.0,
-) -> torch.Tensor:
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Fused computation of g and beta for Gated Delta Net.
+    g = -self.A_log.float().exp() * F.softplus(a.float() + self.dt_bias)
+    beta_output = b.sigmoid()
+    TODO maybe use torch.compile to replace this triton kernel
+    """
     batch, num_heads = a.shape
     seq_len = 1
     grid = (batch, seq_len, triton.cdiv(num_heads, 8))
-    g = torch.empty_like(a, dtype=torch.float32)
+    g = torch.empty(1, batch, num_heads, dtype=torch.float32, device=a.device)
+    beta_output = torch.empty(1, batch, num_heads, dtype=torch.float32, device=b.device)
     fused_gdn_gating_kernel[grid](
-        g, A_log, a, dt_bias, seq_len, num_heads, beta, threshold, 8, num_warps=1
+        g,
+        beta_output,
+        A_log,
+        a,
+        b,
+        dt_bias,
+        seq_len,
+        num_heads,
+        beta,
+        threshold,
+        8,
+        num_warps=1,
     )
-    return g
+    return g, beta_output