diff --git a/python/sglang/srt/layers/quantization/fp8_utils.py b/python/sglang/srt/layers/quantization/fp8_utils.py
index 0a3eccf57..e924d646c 100755
--- a/python/sglang/srt/layers/quantization/fp8_utils.py
+++ b/python/sglang/srt/layers/quantization/fp8_utils.py
@@ -1552,6 +1552,13 @@ def requant_weight_ue8m0(
 ):
     assert weight_block_size == [128, 128]
 
+    # 3D+ weights stack multiple experts (e.g. MoE); requant each group separately.
+    # 2D weights are a single matrix and fall through to the direct path below.
+    if weight.dim() > 2:
+        return _requant_weight_ue8m0_grouped(
+            weight, weight_scale_inv, weight_block_size
+        )
+
     *_, n, k = weight.shape
 
     weight_dequant = block_quant_dequant(
@@ -1571,6 +1578,47 @@ def requant_weight_ue8m0(
     return out_w, out_s
 
 
+def _requant_weight_ue8m0_grouped(
+    weight: torch.Tensor,
+    weight_scale_inv: torch.Tensor,
+    weight_block_size: List[int],
+):
+    *group_dims, n, k = weight.shape
+    w_groups = weight.reshape(-1, n, k)
+    s_groups = weight_scale_inv.reshape(-1, *weight_scale_inv.shape[-2:])
+    num_groups = w_groups.shape[0]
+
+    out_w = None
+    out_s = None
+    for g in range(num_groups):
+        weight_dequant = block_quant_dequant(
+            w_groups[g],
+            s_groups[g],
+            weight_block_size,
+            torch.bfloat16,
+        )
+        w_g, s_g = quant_weight_ue8m0(
+            weight_dequant=weight_dequant,
+            weight_block_size=weight_block_size,
+        )
+        if out_w is None:
+            out_w = torch.empty(
+                (num_groups, *w_g.shape), dtype=w_g.dtype, device=w_g.device
+            )
+            out_s = torch.empty(
+                (num_groups, *s_g.shape), dtype=s_g.dtype, device=s_g.device
+            )
+        out_w[g] = w_g
+        out_s[g] = s_g
+
+    out_w = out_w.view(*group_dims, n, k)
+    out_s = out_s.view(*group_dims, *out_s.shape[-2:])
+
+    out_s = transform_scale_ue8m0(out_s, mn=n)
+
+    return out_w, out_s
+
+
 def quant_weight_ue8m0(
     weight_dequant: torch.Tensor,
     weight_block_size: List[int],