diff --git a/vllm/models/deepseek_v4/attention.py b/vllm/models/deepseek_v4/attention.py
index 913b506dc65..346b196b3c8 100644
--- a/vllm/models/deepseek_v4/attention.py
+++ b/vllm/models/deepseek_v4/attention.py
@@ -47,6 +47,7 @@ from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.models.utils import extract_layer_index
 from vllm.models.deepseek_v4.common.rope import build_deepseek_v4_rope
 from vllm.models.deepseek_v4.compressor import DeepseekCompressor
+from vllm.triton_utils import tl, triton
 from vllm.utils.multi_stream_utils import (
     execute_in_parallel,
     maybe_execute_in_parallel,
@@ -66,6 +67,25 @@ from vllm.v1.kv_cache_interface import (
 logger = init_logger(__name__)
 
 
+@triton.jit
+def _fill_short_context_topk_indices(
+    output,
+    positions,
+    TOP_K: tl.constexpr,
+    COMPRESS_RATIO: tl.constexpr,
+    PADDED_TOP_K: tl.constexpr,
+):
+    # small triton kernel that selects every candidate, -1 otherwise
+    row = tl.program_id(0)
+    offsets = tl.arange(0, PADDED_TOP_K)
+    num_compressed = (tl.load(positions + row) + 1) // COMPRESS_RATIO
+    tl.store(
+        output + row * TOP_K + offsets,
+        tl.where(offsets < num_compressed, offsets, -1),
+        mask=offsets < TOP_K,
+    )
+
+
 def _resolve_dsv4_kv_cache_dtype(
     use_fp8_ds_mla_layout: bool,
     kv_cache_dtype: str,
@@ -787,6 +807,29 @@ class DeepseekV4Indexer(nn.Module):
     ) -> torch.Tensor:
         compressor = self.compressor
 
+        attn_metadata = get_forward_context().attn_metadata
+        if isinstance(attn_metadata, dict):
+            indexer_metadata = cast(Any, attn_metadata[self.k_cache.prefix])
+            if indexer_metadata.max_seq_len // self.compress_ratio <= self.topk_tokens:
+                # candidates num smaller than topk, every candidate is selected
+                # but we still need to build k cache
+                compressor(compressed_kv_score, positions, rotary_emb)
+                assert self.topk_indices_buffer is not None
+                num_tokens = (
+                    indexer_metadata.num_decode_tokens
+                    + indexer_metadata.num_prefill_tokens
+                )
+                if num_tokens > 0:
+                    _fill_short_context_topk_indices[(num_tokens,)](
+                        self.topk_indices_buffer,
+                        positions,
+                        TOP_K=self.topk_tokens,
+                        COMPRESS_RATIO=self.compress_ratio,
+                        PADDED_TOP_K=triton.next_power_of_2(self.topk_tokens),
+                        num_warps=8,
+                    )
+                return self.topk_indices_buffer
+
         def wq_b_and_q_quant():
             # ReplicatedLinear returns (output, bias); bias is None.
             q, _ = self.wq_b(qr)