diff --git a/src/transformers/integrations/deepspeed.py b/src/transformers/integrations/deepspeed.py
index 7a64a753c6d4..9703f642f8bc 100644
--- a/src/transformers/integrations/deepspeed.py
+++ b/src/transformers/integrations/deepspeed.py
@@ -491,7 +491,7 @@ def load(module: nn.Module, state_dict, prefix="", assign_to_params_buffers=Fals
             for k in named_parameters:
                 if k in state_dict:
                     param = named_parameters[k]
-                    # crutial to not init the weight again
+                    # crucial to not init the weight again
                     param._is_hf_initialized = True
                     params_to_gather.append(param)
                     missing_keys.discard(k)
@@ -504,6 +504,15 @@ def load(module: nn.Module, state_dict, prefix="", assign_to_params_buffers=Fals
                     if torch.distributed.get_rank() == 0:
                         module._load_from_state_dict(*args)
 
+            # Buffers are not partitioned by ZeRO-3, load them directly
+            named_buffers = dict(module.named_buffers(prefix=prefix[:-1], recurse=False))
+            for k, buf in named_buffers.items():
+                if k in state_dict and buf is not None:
+                    missing_keys.discard(k)
+                    with torch.no_grad():
+                        buf.copy_(state_dict[k])
+                    buf._is_hf_initialized = True
+
         for name, child in module._modules.items():
             if child is not None:
                 load(child, state_dict, prefix + name + ".", assign_to_params_buffers)
diff --git a/tests/trainer/distributed/test_trainer_distributed_deepspeed.py b/tests/trainer/distributed/test_trainer_distributed_deepspeed.py
index 8d3672a55c26..6a0b3c49160e 100644
--- a/tests/trainer/distributed/test_trainer_distributed_deepspeed.py
+++ b/tests/trainer/distributed/test_trainer_distributed_deepspeed.py
@@ -1526,6 +1526,60 @@ def test_resize_token_embeddings_zero3(self):
         with deepspeed.zero.GatheredParameters([embedding.weight]):
             self.assertEqual(embedding.weight.shape[0], new_size)
 
+    def test_zero3_load_registered_buffers(self):
+        """Test that registered buffers are loaded with correct values under ZeRO-3 from_pretrained."""
+        from transformers.models.gemma4.configuration_gemma4 import (
+            Gemma4AudioConfig,
+            Gemma4Config,
+            Gemma4TextConfig,
+            Gemma4VisionConfig,
+        )
+        from transformers.models.gemma4.modeling_gemma4 import Gemma4ForConditionalGeneration
+
+        text_config = Gemma4TextConfig(
+            hidden_size=128,
+            num_hidden_layers=2,
+            num_attention_heads=2,
+            intermediate_size=256,
+            vocab_size=32000,
+            num_key_value_heads=2,
+            pad_token_id=0,
+        )
+        vision_config = Gemma4VisionConfig(
+            hidden_size=64, num_hidden_layers=2, num_attention_heads=2, intermediate_size=128
+        )
+        audio_config = Gemma4AudioConfig()
+        config = Gemma4Config(text_config=text_config, vision_config=vision_config, audio_config=audio_config)
+
+        # Save without ZeRO-3, with non-default buffer values
+        save_path = self.get_auto_remove_tmp_dir()
+        model = Gemma4ForConditionalGeneration(config)
+        for name, buf in model.named_buffers():
+            if "input_max" in name:
+                buf.fill_(42.0)
+            elif "output_min" in name:
+                buf.fill_(-42.0)
+            elif "layer_scalar" in name:
+                buf.fill_(0.5)
+        model.save_pretrained(save_path)
+        del model
+
+        # Load with ZeRO-3
+        ds_config = self._get_zero3_ds_config(bf16={"enabled": True})
+        dschf = HfDeepSpeedConfig(ds_config)
+        self.assertTrue(dschf.is_zero3())
+        with mockenv_context(**self.dist_env_1_gpu):
+            model2 = Gemma4ForConditionalGeneration.from_pretrained(save_path, torch_dtype=torch.bfloat16)
+
+        # Verify buffer VALUES were loaded from checkpoint, not re-initialized
+        for name, buf in model2.named_buffers():
+            if "input_max" in name:
+                self.assertEqual(buf.item(), 42.0, f"{name} was not loaded from checkpoint")
+            elif "output_min" in name:
+                self.assertEqual(buf.item(), -42.0, f"{name} was not loaded from checkpoint")
+            elif "layer_scalar" in name:
+                self.assertEqual(buf.item(), 0.5, f"{name} was not loaded from checkpoint")
+
 
 # ---------------------------------------------------------------------------
 # Model Zoo — test many architectures with DeepSpeed + zero_to_fp32 recovery