diff --git a/vllm/model_executor/models/gpt_oss.py b/vllm/model_executor/models/gpt_oss.py
index 863d8ea3ba7f..01f2752ac54b 100644
--- a/vllm/model_executor/models/gpt_oss.py
+++ b/vllm/model_executor/models/gpt_oss.py
@@ -413,14 +413,6 @@ def _load_weights_mxfp4(
             if is_pp_missing_parameter(name, self):
                 continue
 
-            # Skip checkpoint weights for layers dropped by an hf_overrides
-            # `num_hidden_layers` prune: the model only builds params for the
-            # layers it keeps, so extra-layer weights have no destination param.
-            if "layers." in name:
-                idx_str = name.split("layers.", 1)[1].split(".", 1)[0]
-                if idx_str.isdigit() and int(idx_str) >= self.config.num_hidden_layers:
-                    continue
-
             if ".w13_weight_scale" in name:
                 # Handle MLP gate and up projection weights scale
                 if use_ep:
@@ -643,13 +635,6 @@ def _get_moe_weight_dtype(layer_id: int = 0) -> str | None:
             if is_pp_missing_parameter(name, self):
                 continue
 
-            # Skip checkpoint weights for layers dropped by an hf_overrides
-            # `num_hidden_layers` prune (mirrors _load_weights_mxfp4).
-            if "layers." in name:
-                idx_str = name.split("layers.", 1)[1].split(".", 1)[0]
-                if idx_str.isdigit() and int(idx_str) >= self.config.num_hidden_layers:
-                    continue
-
             layer_id, expert_id, fused_name = None, None, None
             moe_quant_method = None
             if "experts" in name:
diff --git a/vllm/models/deepseek_v4/amd/model.py b/vllm/models/deepseek_v4/amd/model.py
index 2c6501452f87..edb923511501 100644
--- a/vllm/models/deepseek_v4/amd/model.py
+++ b/vllm/models/deepseek_v4/amd/model.py
@@ -626,16 +626,6 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         expert_mapping = self.get_expert_mapping()
 
         for name, loaded_weight in weights:
-            # Skip checkpoint weights for transformer layers pruned by a
-            # num_hidden_layers hf_override (keeps load + run tractable on the
-            # FFM simulator). Names here are already vLLM-mapped ("layers.N.").
-            if "layers." in name:
-                try:
-                    _li = int(name.split("layers.", 1)[1].split(".", 1)[0])
-                    if _li >= self.config.num_hidden_layers:
-                        continue
-                except (IndexError, ValueError):
-                    pass
             for param_name, weight_name, shard_id in stacked_params_mapping:
                 # Skip non-stacked layers and experts (experts handled below).
                 if ".experts." in name:
@@ -692,10 +682,6 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                 elif "attn_sink" in name:
                     if is_pp_missing_parameter(name, self):
                         continue
-                    if name not in params_dict:
-                        # Layer pruned via a num_hidden_layers hf_override; skip
-                        # its checkpoint weights instead of KeyError-ing.
-                        continue
                     narrow_weight = loaded_weight[head_rank_start:head_rank_end]
                     n = narrow_weight.shape[0]
                     params_dict[name][:n].copy_(narrow_weight)
@@ -704,9 +690,6 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                 else:
                     if is_pp_missing_parameter(name, self):
                         continue
-                    if name not in params_dict:
-                        # Layer pruned via a num_hidden_layers hf_override; skip.
-                        continue
                     param = params_dict[name]
                     weight_loader = getattr(
                         param, "weight_loader", default_weight_loader