diff --git a/python/sglang/srt/layers/attention/nsa/nsa_indexer.py b/python/sglang/srt/layers/attention/nsa/nsa_indexer.py
index b80d3bdc9..4d7cb9323 100644
--- a/python/sglang/srt/layers/attention/nsa/nsa_indexer.py
+++ b/python/sglang/srt/layers/attention/nsa/nsa_indexer.py
@@ -182,12 +182,11 @@ class Indexer(MultiPlatformOp):
             quant_config=quant_config,
             prefix=add_prefix("wk", prefix),
         )
-        # NOTE: weights_proj in the checkpoint is stored in bf16, while the parameters here are stored in fp32 for convenience
         self.weights_proj = ReplicatedLinear(
             self.hidden_size,
             self.n_heads,
             bias=False,
-            params_dtype=torch.float32,
+            params_dtype=torch.bfloat16,
             prefix=add_prefix("weights_proj", prefix),
         )
         self.k_norm = LayerNorm(self.head_dim, dtype=torch.float32)
@@ -221,13 +220,15 @@ class Indexer(MultiPlatformOp):
 
     @torch.compile(dynamic=True) if not _is_hip else lambda f: f
     def _project_and_scale_head_gates(self, x: torch.Tensor):
-        weights, _ = self.weights_proj(x.float())
+        weights, _ = self.weights_proj(x)
+        weights = weights.float()
         weights = weights * self.n_heads**-0.5
         return weights
 
     @torch.compile(dynamic=True) if not _is_hip else lambda f: f
     def _get_logits_head_gate(self, x: torch.Tensor, q_scale: torch.Tensor):
-        weights, _ = self.weights_proj(x.float())
+        weights, _ = self.weights_proj(x)
+        weights = weights.float()
         weights = weights * self.n_heads**-0.5
         weights = weights.unsqueeze(-1) * q_scale * self.softmax_scale
         return weights