diff --git a/src/maxtext/layers/attention_mla.py b/src/maxtext/layers/attention_mla.py
index db6afd813c..8a486e3ac3 100644
--- a/src/maxtext/layers/attention_mla.py
+++ b/src/maxtext/layers/attention_mla.py
@@ -76,6 +76,71 @@
 from maxtext.utils.globals import EPS
 
 
+def _apply_rope(x, cos, sin, interleave=True):
+  """Applies rotary positional embedding to the input.
+
+  Args:
+    x: Input tensor [B, S, N, H] or [B, S, H].
+    cos: Cosine component of RoPE, [B, S, 1, H/2] or [B, S, H/2].
+    sin: Sine component of RoPE, [B, S, 1, H/2] or [B, S, H/2].
+    interleave: Whether to use interleaved or concatenated layout.
+
+  Returns:
+    Rotated input.
+  """
+  if interleave:
+    x1, x2 = x[..., ::2], x[..., 1::2]
+  else:
+    x1, x2 = jnp.split(x, 2, axis=-1)
+
+  # Handle cases with or without heads dimension
+  if x.ndim == 4:
+    cos = cos[:, :, None, :] if cos.ndim == 3 else cos
+    sin = sin[:, :, None, :] if sin.ndim == 3 else sin
+  elif x.ndim == 3:
+    cos = cos[:, :, :] if cos.ndim == 3 else cos
+    sin = sin[:, :, :] if sin.ndim == 3 else sin
+
+  y1 = x1 * cos - x2 * sin
+  y2 = x1 * sin + x2 * cos
+
+  if interleave:
+    rotated = jnp.stack([y1, y2], axis=-1)
+    return rotated.reshape(x.shape)
+  else:
+    return jnp.concatenate([y1, y2], axis=-1)
+
+
+def _compute_rope(head_dim, positions, theta, dtype):
+  """Computes RoPE frequencies on the fly for given positions."""
+  freqs = 1.0 / (
+      theta ** (jnp.arange(0, head_dim, 2, dtype=jnp.float32) / head_dim)
+  )
+  # positions shape [B, S], freqs shape [D/2] -> angles shape [B, S, D/2]
+  angles = positions[..., None] * freqs
+  return jnp.cos(angles).astype(dtype), jnp.sin(angles).astype(dtype)
+
+
+def _get_cos_sin(rotary_embedding, positions, dtype):
+  """Computes cos and sin embeddings from the rotary_embedding module."""
+  # Use optimized on-the-fly computation instead of table lookup
+  head_dim = rotary_embedding.embedding_dims
+  theta = rotary_embedding.rope_theta
+
+  cos, sin = _compute_rope(head_dim, positions, theta, dtype)
+
+  # Add heads dimension for broadcasting: [B, S, D/2] -> [B, S, 1, D/2]
+  cos = cos[:, :, jnp.newaxis, :]
+  sin = sin[:, :, jnp.newaxis, :]
+
+  if getattr(rotary_embedding, "attention_scaling", False):
+    rope_factor = getattr(rotary_embedding, "rope_factor", 1.0)
+    scaling = 1.0 if rope_factor <= 1 else (0.1 * math.log(rope_factor) + 1.0)
+    cos = cos * scaling
+    sin = sin * scaling
+  return cos, sin
+
+
 class Indexer(nnx.Module):
   """Indexer for DeepSeek Sparse Attention (DSA).
 
@@ -189,9 +254,10 @@ def apply_partial_rope(
     # indexer_head_dim -> [rope_head_dim, indexer_head_dim - rope_head_dim]
     x_pe, x_nope = jnp.split(inputs, [self.rope_head_dim], axis=-1)
     # x_pe [B, S, H, rope_head_dim], positions [B, S]
-    x_pe = self.rotary_embedding(x_pe, position=inputs_positions)
+    cos, sin = _get_cos_sin(self.rotary_embedding, inputs_positions, self.dtype)
+    x_pe = _apply_rope(x_pe, cos, sin, interleave=self.rotary_embedding.interleave)
     x = jnp.concatenate([x_pe, x_nope], axis=-1)
-    return x
+    return checkpoint_name(x, "indexer_partial_rope")
 
   def generate_mask(self, topk_indices, s):
     """
@@ -478,6 +544,17 @@ def mla_as_linen(
 class MLA(Attention):
   """Multi-Head Latent Attention (MLA) layer."""
 
+  def apply_rotary_embedding(self, inputs: Array, inputs_positions: Optional[Array | None] = None, rope_kwargs: dict | None = None):
+    """Overrides RoPE with optimized implementation for MLA."""
+    with jax.named_scope("mla_rope"):
+      if inputs_positions is None:
+        seq_length = inputs.shape[1]
+        inputs_positions = jnp.arange(seq_length, dtype=jnp.int32)[jnp.newaxis, :]
+
+      cos, sin = _get_cos_sin(self.rotary_embedding, inputs_positions, self.dtype)
+      x_out = _apply_rope(inputs, cos, sin, interleave=self.rotary_embedding.interleave)
+      return checkpoint_name(x_out, "mla_rope")
+
   def __init__(
       self,
       config: Config,