diff --git a/vllm/v1/attention/backends/mla/common.py b/vllm/v1/attention/backends/mla/common.py
index f2610671f769e..646e4fec836bd 100755
--- a/vllm/v1/attention/backends/mla/common.py
+++ b/vllm/v1/attention/backends/mla/common.py
@@ -416,7 +416,6 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
         self.model_config = vllm_config.model_config
         cache_config = vllm_config.cache_config
         parallel_config = vllm_config.parallel_config
-        self.chunked_prefill_enabled = scheduler_config.chunked_prefill_enabled
         self.num_heads = self.model_config.get_num_attention_heads(
             parallel_config)
         self.mla_dims = get_mla_dims(self.model_config)
@@ -426,30 +425,28 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
         if self.aot_schedule:
             self.page_size = self.kv_cache_spec.block_size
 
-        if self.chunked_prefill_enabled:
-            self.chunked_prefill_workspace_size = min(
-                # Max sure there is enough for 8 full length request or at least
-                # 4 pages of cache per request
-                max(
-                    8 * self.model_config.max_model_len, 4 *
-                    scheduler_config.max_num_seqs * cache_config.block_size),
-                # For long-context models try not to over-allocate limiting
-                # kv-cache space, limiting it to 64k tokens,
-                # which would result in the workspace being:
-                #   2*(576)*(64*1024) = 144mb
-                # (assuming 576 MLA head dim, and fp16)
-                # which would result in up-projected context being
-                #   2*(192*128)*(64*1024) = 3gb
-                # (assuming 192 QK head dim, 128 heads, and fp16)
-                128 * 1024)
-            assert self.chunked_prefill_workspace_size >= \
-                scheduler_config.max_num_seqs * cache_config.block_size
-            self.chunked_prefill_workspace = torch.empty(
-                (self.chunked_prefill_workspace_size,
-                 self.model_config.get_head_size()),
-                dtype=self.model_config.dtype,
-                device=device,
-            )
+        self.chunked_prefill_workspace_size = min(
+            # Max sure there is enough for 8 full length request or at least
+            # 4 pages of cache per request
+            max(8 * self.model_config.max_model_len,
+                4 * scheduler_config.max_num_seqs * cache_config.block_size),
+            # For long-context models try not to over-allocate limiting
+            # kv-cache space, limiting it to 64k tokens,
+            # which would result in the workspace being:
+            #   2*(576)*(64*1024) = 144mb
+            # (assuming 576 MLA head dim, and fp16)
+            # which would result in up-projected context being
+            #   2*(192*128)*(64*1024) = 3gb
+            # (assuming 192 QK head dim, 128 heads, and fp16)
+            128 * 1024)
+        assert self.chunked_prefill_workspace_size >= \
+            scheduler_config.max_num_seqs * cache_config.block_size
+        self.chunked_prefill_workspace = torch.empty(
+            (self.chunked_prefill_workspace_size,
+             self.model_config.get_head_size()),
+            dtype=self.model_config.dtype,
+            device=device,
+        )
 
         self._use_cudnn_prefill = use_cudnn_prefill()
         self._use_fi_prefill = use_flashinfer_prefill()
@@ -620,8 +617,7 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
                 reqs_start:] - query_start_loc[reqs_start]
 
             chunked_context_metadata = None
-            if self.chunked_prefill_enabled and num_prefills > 0 \
-                and max_context_len_cpu > 0:
+            if max_context_len_cpu > 0:
                 # NOTE: it is recommend you read the `Chunked Prefill` section
                 # in the comment at the top of the file before trying to
                 # understand the following code