diff --git a/vllm_ascend/spec_decode/eagle_proposer.py b/vllm_ascend/spec_decode/eagle_proposer.py
index 789dd091..fb2928e6 100644
--- a/vllm_ascend/spec_decode/eagle_proposer.py
+++ b/vllm_ascend/spec_decode/eagle_proposer.py
@@ -226,19 +226,49 @@ class EagleProposer(VllmEagleProposer):
                 )
             # If pp>1, the weights of mtp and the main model's embedding are not on the same device.
             # check if mtp model use main model's embedding and LMhead
-            if hasattr(model, "model") and hasattr(model.model, "embed_tokens") and \
-                    torch.equal(self.model.model.embed_tokens.weight,
-                                model.model.embed_tokens.weight):
-                logger.info(
-                    "The EAGLE head shares the same vocab embedding" \
-                    " with the target model."
-                )
-                self.model.model.embed_tokens = target_embed_tokens
+            share_embeddings = False
+            if hasattr(self.model, "has_own_embed_tokens"):
+                # EAGLE model
+                if not self.model.has_own_embed_tokens:
+                    share_embeddings = True
+                    logger.info(
+                        "Detected EAGLE model without its own embed_tokens in the"
+                        " checkpoint. Sharing target model embedding weights with the"
+                        " draft model."
+                    )
+                elif (
+                    isinstance(target_embed_tokens.weight, torch.Tensor)
+                    and isinstance(self.model.model.embed_tokens.weight, torch.Tensor)
+                    # TODO: Offload to CPU for comparison to avoid extra NPU memory
+                    # usage in CI testing environments with limited NPU memory
+                    and torch.equal(
+                        target_embed_tokens.weight.cpu(),
+                        self.model.model.embed_tokens.weight.cpu(),
+                    )
+                ):
+                    share_embeddings = True
+                    logger.info(
+                        "Detected EAGLE model with embed_tokens identical to the target"
+                        " model. Sharing target model embedding weights with the draft"
+                        " model."
+                    )
+                else:
+                    logger.info(
+                        "Detected EAGLE model with distinct embed_tokens weights. "
+                        "Keeping separate embedding weights from the target model."
+                    )
             else:
+                # MTP model
+                share_embeddings = True
                 logger.info(
-                    " The EAGLE head loaded its own vocab embedding" \
-                    " weights instead of sharing them with the target model."
+                    "Detected MTP model. "
+                    "Sharing target model embedding weights with the draft model."
                 )
+
+            if share_embeddings:
+                if hasattr(self.model.model, "embed_tokens"):
+                    del self.model.model.embed_tokens
+                self.model.model.embed_tokens = target_embed_tokens
         else:
             logger.info(
                 "Since PP > 1 or other reasons the model head loaded its own vocab embedding" \