diff --git a/vllm/model_executor/layers/rotary_embedding.py b/vllm/model_executor/layers/rotary_embedding.py
index fa85f72e..e6ee2b96 100644
--- a/vllm/model_executor/layers/rotary_embedding.py
+++ b/vllm/model_executor/layers/rotary_embedding.py
@@ -734,34 +734,50 @@ class GemmaRotaryEmbedding(RotaryEmbedding):
         return inv_freq
 
 
-class ExtendedRotaryEmbedding(RotaryEmbedding):
+class Llama3RotaryEmbedding(RotaryEmbedding):
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position_embeddings: int,
+        base: int,
+        is_neox_style: bool,
+        dtype: torch.dtype,
+        scaling_factor: float,
+        low_freq_factor: float,
+        high_freq_factor: float,
+        orig_max_position: int,
+    ) -> None:
+        self.scaling_factor = scaling_factor
+        self.low_freq_factor = low_freq_factor
+        self.high_freq_factor = high_freq_factor
+        self.orig_max_position = orig_max_position
+        super().__init__(head_size, rotary_dim, max_position_embeddings, base,
+                         is_neox_style, dtype)
 
     def _compute_inv_freq(self, base: Union[int, float]) -> torch.Tensor:
         inv_freqs = super()._compute_inv_freq(base)
-        return self.apply_scaling(inv_freqs)
+        low_freq_wavelen = self.orig_max_position / self.low_freq_factor
+        high_freq_wavelen = self.orig_max_position / self.high_freq_factor
 
-    def apply_scaling(self, freqs: torch.Tensor):
-        scale_factor = 8
-        low_freq_factor = 1
-        high_freq_factor = 4
-        old_context_len = 8192
-
-        low_freq_wavelen = old_context_len / low_freq_factor
-        high_freq_wavelen = old_context_len / high_freq_factor
-        new_freqs = []
-        for freq in freqs:
-            wavelen = 2 * math.pi / freq
-            if wavelen < high_freq_wavelen:
-                new_freqs.append(freq)
-            elif wavelen > low_freq_wavelen:
-                new_freqs.append(freq / scale_factor)
-            else:
-                assert low_freq_wavelen != high_freq_wavelen
-                smooth = (old_context_len / wavelen - low_freq_factor) / (
-                    high_freq_factor - low_freq_factor)
-                new_freqs.append((1 - smooth) * freq / scale_factor +
-                                 smooth * freq)
-        return torch.tensor(new_freqs, dtype=freqs.dtype, device=freqs.device)
+        wave_len = 2 * math.pi / inv_freqs
+        if self.low_freq_factor != self.high_freq_factor:
+            smooth = (self.orig_max_position / wave_len - self.low_freq_factor
+                      ) / (self.high_freq_factor - self.low_freq_factor)
+        else:
+            smooth = 0
+        new_freqs = torch.where(
+            wave_len < high_freq_wavelen,
+            inv_freqs,
+            torch.where(
+                wave_len > low_freq_wavelen,
+                inv_freqs / self.scaling_factor,
+                (1 - smooth) * inv_freqs / self.scaling_factor +
+                smooth * inv_freqs,
+            ),
+        )
+        return new_freqs
 
 
 _ROPE_DICT: Dict[Tuple, RotaryEmbedding] = {}
@@ -794,6 +810,7 @@ def get_rope(
            rope_scaling_args, dtype)
     if key in _ROPE_DICT:
         return _ROPE_DICT[key]
+
     if rope_scaling is None:
         rotary_emb = RotaryEmbedding(head_size, rotary_dim, max_position, base,
                                      is_neox_style, dtype)
@@ -802,12 +819,19 @@ def get_rope(
             "type"] if "type" in rope_scaling else rope_scaling["rope_type"]
         # The correct one should be "longrope" but keep "su" here
         # for backward compatible
-        if scaling_type not in {"su", "longrope", "llama3"}:
+        if scaling_type not in {"su", "longrope"}:
             scaling_factor = rope_scaling["factor"]
         if scaling_type == "llama3":
-            rotary_emb = ExtendedRotaryEmbedding(head_size, rotary_dim,
-                                                 max_position, base,
-                                                 is_neox_style, dtype)
+            low_freq_factor = rope_scaling["low_freq_factor"]
+            high_freq_factor = rope_scaling["high_freq_factor"]
+            original_max_position = rope_scaling[
+                "original_max_position_embeddings"]
+            rotary_emb = Llama3RotaryEmbedding(head_size, rotary_dim,
+                                               max_position, base,
+                                               is_neox_style, dtype,
+                                               scaling_factor, low_freq_factor,
+                                               high_freq_factor,
+                                               original_max_position)
         elif scaling_type == "linear":
             rotary_emb = LinearScalingRotaryEmbedding(head_size, rotary_dim,
                                                       max_position, base,