diff --git a/dsv4/layers/moe.py b/dsv4/layers/moe.py
index 3d1b33fb..1744e767 100644
--- a/dsv4/layers/moe.py
+++ b/dsv4/layers/moe.py
@@ -90,6 +90,7 @@ class Nvfp4MoE:
         self._padded_x_sf_buf_l2 = None
         self._l1_gsa_buf = None
         self._l2_gsa_buf = None
+        self._l1_out_buf = None  # pre-allocated L1 GEMM output for graph capture
         self._output_buf = None
         self._row_indices_buf = None
         self._padded_hidden_buf = None
@@ -164,6 +165,13 @@ class Nvfp4MoE:
         self._l1_gsa_buf = torch.zeros(self.num_experts, dtype=torch.float32, device=self.device)
         self._l2_gsa_buf = torch.zeros(self.num_experts, dtype=torch.float32, device=self.device)
         
+        # Pre-allocated L1 GEMM output — avoids torch.zeros() in run_fused_swiglu_grouped_gemm
+        # Shape: (max_tokens * top_k, intermediate_size) — max possible L1 output
+        self._l1_out_buf = torch.zeros(
+            self.max_num_tokens * self.top_k, self.intermediate_size,
+            dtype=torch.bfloat16, device=self.device
+        )
+        
         # Pre-allocated tokens-per-expert buffer — replaces torch.bincount
         # (bincount produces data-dependent shapes, breaks CUDA graph capture)
         self._tokens_per_expert_buf = torch.zeros(self.num_experts, dtype=torch.int32, device=self.device)
@@ -648,6 +656,7 @@ class Nvfp4MoE:
                 expert_offsets=padded_expert_offsets[1:self.num_experts + 1],
                 global_scale_a=l1_gsa, global_scale_b=self._l1_gsb,
                 swiglu_limit=self._swiglu_limit if self._swiglu_limit is not None else 0.0,
+                out=self._l1_out_buf,
             )
             l1_out_real = l1_out[padded_dst]
             # Fused deinterleave + amax + quantize: zero CPU syncs.
diff --git a/dsv4/layers/shared_expert.py b/dsv4/layers/shared_expert.py
index cb936673..4d32a548 100644
--- a/dsv4/layers/shared_expert.py
+++ b/dsv4/layers/shared_expert.py
@@ -91,6 +91,9 @@ class Nvfp4SharedExpert:
         self._l1_activation_global_scale = 1.0 / (6.0 * 448.0)
         self._l2_activation_global_scale = 1.0 / (6.0 * 448.0)
 
+        # Pre-allocated L1 GEMM output for graph capture
+        self._l1_out_buf = None
+
         # Pre-allocated cudagraph buffers (set in _allocate_buffers)
         self._padded_x_fp4_buf_l1 = None
         self._padded_x_sf_buf_l1 = None
@@ -179,6 +182,12 @@ class Nvfp4SharedExpert:
         # Global scale buffers
         self._l1_gsa_buf = torch.zeros(1, dtype=torch.float32, device=self.device)
         self._l2_gsa_buf = torch.zeros(1, dtype=torch.float32, device=self.device)
+        
+        # Pre-allocated L1 output buffer for graph capture
+        self._l1_out_buf = torch.zeros(
+            max_rows, self.intermediate_size,
+            dtype=torch.bfloat16, device=self.device
+        )
 
         # Expert offsets for num_groups=1: just [num_tokens_padded]
         # The GEMM expects expert_offsets as (num_experts,) cumulative offsets
@@ -290,6 +299,7 @@ class Nvfp4SharedExpert:
             global_scale_a=gsa,
             global_scale_b=self._l1_gsb,
             swiglu_limit=self.swiglu_limit if self.swiglu_limit is not None else 0.0,
+            out=self._l1_out_buf,
         )
         l1_out_real = l1_out[:num_tokens]  # (num_tokens, 2*intermediate) BF16, interleaved [silu(gate), silu(gate)*up]
         # Deinterleave to separate gate and up, then take up half (SwiGLU result)