diff --git a/python/sglang/srt/lora/backend/ascend_backend.py b/python/sglang/srt/lora/backend/ascend_backend.py
index 1c2464f43..4aa35dbb3 100644
--- a/python/sglang/srt/lora/backend/ascend_backend.py
+++ b/python/sglang/srt/lora/backend/ascend_backend.py
@@ -21,6 +21,47 @@ class AscendLoRABackend(BaseLoRABackend):
     ):
         super().__init__(max_loras_per_batch, device)
 
+    def run_lora_a_embedding(
+        self, input_ids, weights, vocab_size, extra_embeddings=None, *args, **kwargs
+    ):
+        assert (
+            extra_embeddings is None
+        ), "Ascend LoRA embedding backend does not support extra embeddings (added tokens)."
+
+        total_seq_len = input_ids.shape[0]
+        if weights.numel() == 0:
+            return torch.zeros(
+                total_seq_len, 0, device=input_ids.device, dtype=weights.dtype
+            )
+
+        num_loras, max_rank, vocab_size_w = weights.shape
+        clamped_ids = input_ids.clamp(0, vocab_size_w - 1).to(torch.int64)
+        token_lora_idx = torch.repeat_interleave(
+            self.batch_info.weight_indices.to(torch.int64),
+            self.batch_info.seg_lens,
+            output_size=total_seq_len,
+        )
+
+        rank_per_token = self.batch_info.lora_ranks[token_lora_idx]
+        scaling_per_token = self.batch_info.scalings[token_lora_idx]
+        rank_idx = torch.arange(max_rank, device=weights.device)
+        token_lora_idx_expanded = token_lora_idx.unsqueeze(1).expand(
+            total_seq_len, max_rank
+        )
+        rank_idx_expanded = rank_idx.unsqueeze(0).expand(total_seq_len, max_rank)
+        clamped_ids_expanded = clamped_ids.unsqueeze(1).expand(total_seq_len, max_rank)
+
+        result = weights[
+            token_lora_idx_expanded, rank_idx_expanded, clamped_ids_expanded
+        ]
+
+        rank_mask = rank_idx.unsqueeze(0) < rank_per_token.unsqueeze(1)
+        result = result * rank_mask.to(result.dtype)
+
+        result = result * scaling_per_token.unsqueeze(1).to(result.dtype)
+
+        return result
+
     def run_lora_a_sgemm(
         self, x: torch.Tensor, weights: torch.Tensor, *args, **kwargs
     ) -> torch.Tensor: