diff --git a/vllm/models/deepseek_v4/compressor.py b/vllm/models/deepseek_v4/compressor.py
index 2482459449d..bc66178f74b 100644
--- a/vllm/models/deepseek_v4/compressor.py
+++ b/vllm/models/deepseek_v4/compressor.py
@@ -173,33 +173,6 @@ class CompressorStateCache(torch.nn.Module, AttentionLayerBase):
 
 
 class DeepseekCompressor(nn.Module):
-    _compressed_kv_buffers: ClassVar[dict[tuple[str, int, int], torch.Tensor]] = {}
-
-    @classmethod
-    def _get_compressed_kv_buffer(
-        cls,
-        device: str,
-        max_num_tokens: int,
-        head_dim: int,
-    ) -> torch.Tensor:
-        if device == "cuda" and torch.accelerator.is_available():
-            device_key = f"cuda:{torch.accelerator.current_device_index()}"
-            alloc_device = torch.device(device_key)
-        else:
-            device_key = str(device)
-            alloc_device = torch.device(device)
-
-        key = (device_key, max_num_tokens, head_dim)
-        buffer = cls._compressed_kv_buffers.get(key)
-        if buffer is None:
-            buffer = torch.empty(
-                (max_num_tokens, head_dim),
-                dtype=torch.float32,
-                device=alloc_device,
-            )
-            cls._compressed_kv_buffers[key] = buffer
-        return buffer
-
     def __init__(
         self,
         vllm_config: VllmConfig,
@@ -276,11 +249,6 @@ class DeepseekCompressor(nn.Module):
             self._fused_sparse_kernel = (
                 _fused_kv_compress_norm_rope_insert_sparse_attn_cutedsl
             )
-            self._compressed_kv_buffer = self._get_compressed_kv_buffer(
-                self.device,
-                vllm_config.scheduler_config.max_num_batched_tokens,
-                self.head_dim,
-            )
             self._quant_block = 64
             self._token_stride = self.nope_head_dim + self.rope_head_dim * 2
             self._scale_dim = self.nope_head_dim // 64 + 1  # 7 real + 1 pad
@@ -407,7 +375,11 @@ class DeepseekCompressor(nn.Module):
                     overlap=self.overlap,
                 )
             else:
-                compressed_kv = self._compressed_kv_buffer[:num_actual]
+                compressed_kv = torch.empty(
+                    (num_actual, self.head_dim),
+                    dtype=torch.float32,
+                    device=state_cache.device,
+                )
                 self._compress_kernel(
                     state_cache,
                     token_to_req_indices,