diff --git a/vllm/model_executor/models/deepseek_eagle.py b/vllm/model_executor/models/deepseek_eagle.py
index 3fb04c3b70dd1..4d7a37292cb02 100644
--- a/vllm/model_executor/models/deepseek_eagle.py
+++ b/vllm/model_executor/models/deepseek_eagle.py
@@ -8,7 +8,6 @@ import torch.nn as nn
 
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import VllmConfig
-from vllm.distributed.parallel_state import get_pp_group
 from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
@@ -172,10 +171,6 @@ class DeepseekV2Model(nn.Module):
                     )
                     break
                 else:
-                    # if PP disabled then draft will share embed with target
-                    if get_pp_group().world_size == 1 and "embed_tokens." in name:
-                        continue
-
                     # Skip loading extra bias for GPTQ models.
                     if name.endswith(".bias") and name not in params_dict:
                         continue
diff --git a/vllm/model_executor/models/llama4_eagle.py b/vllm/model_executor/models/llama4_eagle.py
index 660c8f1bb5226..0146b30579287 100644
--- a/vllm/model_executor/models/llama4_eagle.py
+++ b/vllm/model_executor/models/llama4_eagle.py
@@ -23,7 +23,6 @@ import torch.nn as nn
 
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import VllmConfig
-from vllm.distributed.parallel_state import get_pp_group
 from vllm.logger import init_logger
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
@@ -127,17 +126,11 @@ class LlamaModel(nn.Module):
                 weight_loader(param, loaded_weight, shard_id)
                 break
             else:
-                # if PP disabled then draft will share embed with target
-                if get_pp_group().world_size == 1 and "embed_tokens." in name:
-                    continue
                 param = params_dict[name]
                 weight_loader = getattr(param, "weight_loader", default_weight_loader)
                 weight_loader(param, loaded_weight)
             loaded_params.add(name)
         for name in params_dict:
-            # if PP disabled then draft will share embed with target
-            if get_pp_group().world_size == 1 and "embed_tokens." in name:
-                continue
             assert name in loaded_params, f"{name} is not loaded!"
         return loaded_params
 
diff --git a/vllm/model_executor/models/llama_eagle.py b/vllm/model_executor/models/llama_eagle.py
index 90ab5c50361b6..05cb456e7776e 100644
--- a/vllm/model_executor/models/llama_eagle.py
+++ b/vllm/model_executor/models/llama_eagle.py
@@ -9,7 +9,6 @@ from transformers import LlamaConfig
 
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import VllmConfig
-from vllm.distributed.parallel_state import get_pp_group
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import ReplicatedLinear
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
@@ -155,10 +154,6 @@ class LlamaModel(nn.Module):
                 weight_loader(param, loaded_weight, shard_id)
                 break
             else:
-                # if PP disabled then draft will share embed with target
-                if get_pp_group().world_size == 1 and "embed_tokens." in name:
-                    continue
-
                 param = params_dict[name]
                 weight_loader = getattr(param, "weight_loader", default_weight_loader)
                 weight_loader(param, loaded_weight)
diff --git a/vllm/v1/spec_decode/eagle.py b/vllm/v1/spec_decode/eagle.py
index 406bb696bd4cf..ba37bc81607fe 100644
--- a/vllm/v1/spec_decode/eagle.py
+++ b/vllm/v1/spec_decode/eagle.py
@@ -1028,8 +1028,11 @@ class EagleProposer:
                 elif (
                     isinstance(target_embed_tokens.weight, torch.Tensor)
                     and isinstance(self.model.model.embed_tokens.weight, torch.Tensor)
-                    and torch.equal(
-                        target_embed_tokens.weight, self.model.model.embed_tokens.weight
+                    and torch.allclose(
+                        target_embed_tokens.weight.cpu(),
+                        self.model.model.embed_tokens.weight.cpu(),
+                        rtol=1e-5,
+                        atol=1e-7,
                     )
                 ):
                     share_embeddings = True