diff --git a/python/sglang/srt/model_executor/runner/prefill_cuda_graph_runner.py b/python/sglang/srt/model_executor/runner/prefill_cuda_graph_runner.py
index 0dd66d48e..43103b99d 100644
--- a/python/sglang/srt/model_executor/runner/prefill_cuda_graph_runner.py
+++ b/python/sglang/srt/model_executor/runner/prefill_cuda_graph_runner.py
@@ -172,7 +172,6 @@ class PrefillCudaGraphRunner(BaseCudaGraphRunner):
             max_bs=self.max_bs,
             max_num_tokens=self.max_num_tokens,
             cache_loc_dtype=self._cache_loc_dtype(),
-            is_hybrid_swa=model_runner.is_hybrid_swa,
             is_multimodal=self.is_multimodal,
             hidden_size=self.model_runner.model_config.hidden_size,
             dtype=self.model_runner.dtype,
diff --git a/python/sglang/srt/model_executor/runner_utils/buffers.py b/python/sglang/srt/model_executor/runner_utils/buffers.py
index a9d0cbd35..728310d66 100644
--- a/python/sglang/srt/model_executor/runner_utils/buffers.py
+++ b/python/sglang/srt/model_executor/runner_utils/buffers.py
@@ -69,7 +69,6 @@ class DecodeInputBuffers(ForwardInputBuffers):
     seq_lens: torch.Tensor
     seq_lens_cpu: torch.Tensor
     out_cache_loc: torch.Tensor
-    out_cache_loc_swa: Optional[torch.Tensor]
     positions: torch.Tensor
     mrope_positions: torch.Tensor
     num_token_non_padded: torch.Tensor
@@ -105,7 +104,6 @@ class DecodeInputBuffers(ForwardInputBuffers):
         cache_loc_dtype: torch.dtype,
         enable_mamba_track: bool,
         ne_token_table: Optional[torch.Tensor] = None,
-        is_hybrid_swa: bool = False,
         hc_hidden_size: Optional[int] = None,
         pp_proxy_topk_size: Optional[int] = None,
     ) -> DecodeInputBuffers:
@@ -115,11 +113,6 @@ class DecodeInputBuffers(ForwardInputBuffers):
             req_pool_indices = torch.zeros((max_bs,), dtype=torch.int64)
             seq_lens = torch.full((max_bs,), seq_len_fill_value, dtype=torch.int64)
             out_cache_loc = torch.zeros((max_num_token,), dtype=cache_loc_dtype)
-            out_cache_loc_swa = (
-                torch.zeros((max_num_token,), dtype=torch.int64)
-                if is_hybrid_swa
-                else None
-            )
             positions = torch.zeros((max_num_token,), dtype=torch.int64)
             mrope_positions = torch.zeros((3, max_num_token), dtype=torch.int64)
             num_token_non_padded = torch.zeros((1,), dtype=torch.int32)
@@ -206,7 +199,6 @@ class DecodeInputBuffers(ForwardInputBuffers):
             seq_lens=seq_lens,
             seq_lens_cpu=seq_lens_cpu,
             out_cache_loc=out_cache_loc,
-            out_cache_loc_swa=out_cache_loc_swa,
             positions=positions,
             mrope_positions=mrope_positions,
             num_token_non_padded=num_token_non_padded,
@@ -326,14 +318,6 @@ class DecodeInputBuffers(ForwardInputBuffers):
                 dsts.append(buf[:dim])
                 srcs.append(src)
 
-        # SWA cache location (int32, separate from the int64 batch above).
-        if (
-            self.out_cache_loc_swa is not None
-            and forward_batch.out_cache_loc_swa is not None
-        ):
-            dsts.append(self.out_cache_loc_swa[:raw_num_token])
-            srcs.append(forward_batch.out_cache_loc_swa[:raw_num_token])
-
         # Batch all GPU copies, grouped by dtype pair.
         _grouped_foreach_copy_(dsts, srcs)
 
@@ -347,7 +331,6 @@ class DecodeInputBuffers(ForwardInputBuffers):
 class PrefillInputBuffers(ForwardInputBuffers):
     input_ids: torch.Tensor
     out_cache_loc: torch.Tensor
-    out_cache_loc_swa: Optional[torch.Tensor]
     mamba_track_indices: Optional[torch.Tensor]
     mamba_track_mask: Optional[torch.Tensor]
     mamba_track_seqlens: Optional[torch.Tensor]
@@ -363,7 +346,6 @@ class PrefillInputBuffers(ForwardInputBuffers):
         max_bs: int,
         max_num_tokens: int,
         cache_loc_dtype: torch.dtype,
-        is_hybrid_swa: bool,
         is_multimodal: bool,
         hidden_size: int,
         dtype: torch.dtype,
@@ -372,11 +354,6 @@ class PrefillInputBuffers(ForwardInputBuffers):
         with torch.device(device):
             input_ids = torch.zeros((max_num_tokens,), dtype=torch.int64)
             out_cache_loc = torch.zeros((max_num_tokens,), dtype=cache_loc_dtype)
-            out_cache_loc_swa = (
-                torch.zeros((max_num_tokens,), dtype=torch.int64)
-                if is_hybrid_swa
-                else None
-            )
             mamba_track_indices = (
                 torch.zeros((max_bs,), dtype=torch.int64)
                 if enable_mamba_track
@@ -402,7 +379,6 @@ class PrefillInputBuffers(ForwardInputBuffers):
         return cls(
             input_ids=input_ids,
             out_cache_loc=out_cache_loc,
-            out_cache_loc_swa=out_cache_loc_swa,
             mamba_track_indices=mamba_track_indices,
             mamba_track_mask=mamba_track_mask,
             mamba_track_seqlens=mamba_track_seqlens,