diff --git a/tests/v1/spec_decode/test_dspark_topk.py b/tests/v1/spec_decode/test_dspark_topk.py
new file mode 100644
index 00000000000..a33483d6db3
--- /dev/null
+++ b/tests/v1/spec_decode/test_dspark_topk.py
@@ -0,0 +1,58 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+from torch import nn
+
+from vllm.model_executor.models.qwen3_dspark import DSparkMarkovHead
+
+
+def _markov_head(weight: torch.Tensor) -> DSparkMarkovHead:
+    head = DSparkMarkovHead.__new__(DSparkMarkovHead)
+    nn.Module.__init__(head)
+    head.markov_w2 = nn.Linear(
+        weight.shape[1], weight.shape[0], bias=False, dtype=weight.dtype
+    )
+    head.markov_w2.weight.data.copy_(weight)
+    return head
+
+
+def test_gathered_markov_bias_overwrites_dense_logits():
+    weight = torch.arange(21, dtype=torch.float32).view(7, 3) / 10
+    markov_embed = torch.tensor([[0.5, -1.0, 0.25], [1.0, 0.5, -0.5]])
+    logits = torch.tensor(
+        [
+            [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7],
+            [0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1],
+        ]
+    )
+    values, index = logits.topk(3, dim=-1)
+    values = torch.stack((values, torch.zeros_like(values)), dim=1)[:, 0]
+    expected = values + torch.bmm(weight[index], markov_embed.unsqueeze(-1)).squeeze(-1)
+    logits.fill_(float("-inf"))
+
+    result = _markov_head(weight).apply_bias_gathered(
+        markov_embed, logits, values, index
+    )
+
+    assert result is logits
+    torch.testing.assert_close(result.gather(1, index), expected)
+    selected = torch.zeros_like(result, dtype=torch.bool).scatter_(1, index, True)
+    assert torch.isneginf(result.masked_select(~selected)).all()
+
+
+def test_gathered_markov_bias_matches_dense_at_full_vocab():
+    weight = torch.arange(15, dtype=torch.float32).view(5, 3) / 10
+    markov_embed = torch.tensor([[0.5, -1.0, 0.25]])
+    logits = torch.tensor([[0.1, 0.4, -0.2, 0.3, 0.0]])
+    original = logits.clone()
+    values, index = logits.topk(logits.shape[-1], dim=-1)
+    scale = 0.5
+    logits.fill_(float("-inf"))
+
+    result = _markov_head(weight).apply_bias_gathered(
+        markov_embed, logits, values, index, scale
+    )
+
+    expected = original + markov_embed @ weight.T * scale
+    torch.testing.assert_close(result, expected)
diff --git a/vllm/config/speculative.py b/vllm/config/speculative.py
index aa3fc67ea11..e6190979684 100644
--- a/vllm/config/speculative.py
+++ b/vllm/config/speculative.py
@@ -289,6 +289,10 @@ class SpeculativeConfig:
     during rejection sampling. This comes at the cost of additional GPU memory
     usage."""
 
+    dspark_draft_topk: int | None = Field(default=None, ge=1)
+    """For Qwen3 DSpark drafting, evaluate the Markov projection only for the
+    top-k base-logit candidates. Requires draft tensor parallel size 1."""
+
     def compute_hash(self) -> str:
         """
         WARNING: Whenever a new field is added to this config,
@@ -1032,6 +1036,61 @@ class SpeculativeConfig:
                         "Inkling MTP currently supports exactly one speculative token"
                     )
 
+                if self.dspark_draft_topk is not None and self.method != "dspark":
+                    raise ValueError("dspark_draft_topk is only supported by DSpark")
+
+                dspark_draft_topk = None
+                if self.method == "dspark":
+                    # DSpark is a semi-autoregressive *block* drafter. A
+                    # speculative length smaller than the checkpoint's block
+                    # feeds the block / Markov-head machinery an unsupported
+                    # layout and yields incorrect (garbled) output rather than
+                    # merely lower acceptance. Require num_speculative_tokens to
+                    # be at least the block size (e.g. 5 or 7 for DeepSeek-V4).
+                    dspark_block_size = getattr(
+                        self.draft_model_config.hf_config,
+                        "dspark_block_size",
+                        None,
+                    )
+                    if (
+                        dspark_block_size is not None
+                        and self.num_speculative_tokens < dspark_block_size
+                    ):
+                        raise ValueError(
+                            "DSpark requires num_speculative_tokens >= "
+                            f"dspark_block_size ({dspark_block_size}); got "
+                            f"{self.num_speculative_tokens}. Smaller values "
+                            "produce incorrect output. Use "
+                            f"num_speculative_tokens={dspark_block_size} or "
+                            "larger (e.g. 7)."
+                        )
+
+                    hf_config = self.draft_model_config.hf_config
+                    dspark_draft_topk = self.dspark_draft_topk
+                    if dspark_draft_topk is None:
+                        dspark_draft_topk = getattr(
+                            hf_config, "dspark_draft_topk", None
+                        )
+                    if dspark_draft_topk is not None:
+                        draft_vocab_size = (
+                            getattr(hf_config, "draft_vocab_size", None)
+                            or hf_config.vocab_size
+                        )
+                        if not 1 <= dspark_draft_topk <= draft_vocab_size:
+                            raise ValueError(
+                                "dspark_draft_topk must be between 1 and the "
+                                f"draft vocabulary size ({draft_vocab_size})"
+                            )
+                        if (
+                            "Qwen3DSparkModel"
+                            not in self.draft_model_config.architectures
+                        ):
+                            raise ValueError(
+                                "dspark_draft_topk is only supported by "
+                                "Qwen3DSparkModel"
+                            )
+                        hf_config.dspark_draft_topk = dspark_draft_topk
+
                 self.draft_tensor_parallel_size = (
                     SpeculativeConfig._verify_and_get_draft_tp(
                         self.target_parallel_config,
@@ -1039,7 +1098,6 @@ class SpeculativeConfig:
                         self.draft_model_config.hf_config,
                     )
                 )
-
                 self.draft_model_config.max_model_len = (
                     SpeculativeConfig._maybe_override_draft_max_model_len(
                         self.max_model_len,
diff --git a/vllm/model_executor/models/qwen3_dspark.py b/vllm/model_executor/models/qwen3_dspark.py
index 04af4566949..03ad01e1d91 100644
--- a/vllm/model_executor/models/qwen3_dspark.py
+++ b/vllm/model_executor/models/qwen3_dspark.py
@@ -77,6 +77,30 @@ class DSparkMarkovHead(nn.Module):
         """Vocab-size transition bias from a Markov embedding ([B, r] -> [B, V])."""
         return logits_processor(self.markov_w2, markov_embed)
 
+    def apply_bias_gathered(
+        self,
+        markov_embed: torch.Tensor,
+        logits: torch.Tensor,
+        values: torch.Tensor,
+        index: torch.Tensor,
+        scale: float = 1.0,
+    ) -> torch.Tensor:
+        """Apply the Markov bias only to selected rows of ``logits``.
+
+        The caller initializes ``logits`` to ``-inf`` once for all draft
+        positions. This method scatters the corrected candidate values into
+        that dense buffer so the normal sampler sees the truncated proposal.
+        """
+        weight = self.markov_w2.weight[index]
+        corrected = values.unsqueeze(-1)
+        corrected.baddbmm_(
+            weight,
+            markov_embed.unsqueeze(-1),
+            beta=1.0,
+            alpha=scale,
+        )
+        return logits.scatter_(1, index, corrected.squeeze(-1))
+
 
 class Qwen3DSparkModel(DFlashQwen3Model):
     """DFlash Qwen3 backbone + DSpark Markov head."""
@@ -158,6 +182,21 @@ class Qwen3DSparkForCausalLM(DFlashQwen3ForCausalLM):
     def markov_bias(self, markov_embed: torch.Tensor) -> torch.Tensor:
         return self.model.markov_head.bias(markov_embed, self.logits_processor)
 
+    def apply_markov_bias_gathered(
+        self,
+        markov_embed: torch.Tensor,
+        logits: torch.Tensor,
+        values: torch.Tensor,
+        index: torch.Tensor,
+    ) -> torch.Tensor:
+        return self.model.markov_head.apply_bias_gathered(
+            markov_embed,
+            logits,
+            values,
+            index,
+            self.logits_processor.scale,
+        )
+
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
         model_weights = {}
         includes_embed_tokens = False
diff --git a/vllm/v1/worker/gpu/spec_decode/dspark/speculator.py b/vllm/v1/worker/gpu/spec_decode/dspark/speculator.py
index 45dedde3d7d..a3207cd3dad 100644
--- a/vllm/v1/worker/gpu/spec_decode/dspark/speculator.py
+++ b/vllm/v1/worker/gpu/spec_decode/dspark/speculator.py
@@ -72,6 +72,9 @@ class DSparkSpeculator(DFlashSpeculator):
         # Reduced-vocab probabilistic drafting only; set in load_draft_model.
         self._d2t_scatter_index: torch.Tensor | None = None
         self._draft_scatter_buf: torch.Tensor | None = None
+        self._draft_topk: int | None = getattr(
+            self.draft_model_config.hf_config, "dspark_draft_topk", None
+        )
 
     def load_draft_model(
         self,
@@ -97,7 +100,43 @@ class DSparkSpeculator(DFlashSpeculator):
             )
         return model
 
+    def _sample_logits(
+        self,
+        logits: torch.Tensor,
+        idx_map: torch.Tensor,
+        sample_pos: torch.Tensor,
+        step: int,
+    ) -> torch.Tensor:
+        if self.draft_logits is None:
+            return self.model.map_draft_to_target(logits.argmax(dim=-1))
+
+        # Probabilistic sampling and rejection operate in target-vocabulary
+        # space. A reduced draft vocabulary is scattered into its target rows.
+        if self._d2t_scatter_index is not None:
+            assert self._draft_scatter_buf is not None
+            buf = self._draft_scatter_buf[: logits.shape[0]]
+            buf.index_copy_(1, self._d2t_scatter_index, logits.to(buf.dtype))
+            logits = buf
+
+        # sample_pos is the predicted token's position Q; the target verifies
+        # it with the predecessor's Gumbel key (Q-1). Pass Q-1.
+        return gumbel_sample(
+            logits,
+            idx_map,
+            self.temperature,
+            self.seeds,
+            sample_pos - 1,
+            apply_temperature=True,
+            output_processed_logits=self.draft_logits,
+            output_processed_logits_col=self._step_cols[step],
+            use_fp64=self.use_fp64_gumbel,
+        )
+
     def _sample_sequential(self, num_reqs: int, head_hidden: torch.Tensor) -> None:
+        if self._draft_topk is not None:
+            self._sample_sequential_topk(num_reqs, head_hidden)
+            return
+
         # Sequential Markov sampling over the backbone's output hidden states.
         n_spec = self.num_speculative_steps
         num_sample = num_reqs * n_spec
@@ -120,31 +159,46 @@ class DSparkSpeculator(DFlashSpeculator):
             markov_embed = self.model.markov_embed(prev)
             bias = self.model.markov_bias(markov_embed)
             logits_i = base_logits[:, i] + bias
-            if self.draft_logits is not None:
-                # Probabilistic: sample in target vocab (a reduced draft vocab is
-                # scattered into its target columns; full vocab is already there).
-                if self._d2t_scatter_index is not None:
-                    assert self._draft_scatter_buf is not None
-                    buf = self._draft_scatter_buf[:num_reqs]
-                    buf.index_copy_(1, self._d2t_scatter_index, logits_i.to(buf.dtype))
-                    logits_i = buf
-                # sample_pos is the predicted token's position Q; the target
-                # verifies it with the predecessor's Gumbel key (Q-1). Pass Q-1.
-                draft_sampled_i = gumbel_sample(
-                    logits_i,
-                    idx_map[:, i],
-                    self.temperature,
-                    self.seeds,
-                    sample_pos[:, i] - 1,
-                    apply_temperature=True,
-                    output_processed_logits=self.draft_logits,
-                    output_processed_logits_col=self._step_cols[i],
-                    use_fp64=self.use_fp64_gumbel,
-                )
-            else:
-                draft_sampled_i = self.model.map_draft_to_target(
-                    logits_i.argmax(dim=-1)
-                )
+            draft_sampled_i = self._sample_logits(
+                logits_i, idx_map[:, i], sample_pos[:, i], i
+            )
+            self.draft_tokens[:num_reqs, i] = draft_sampled_i
+            prev = draft_sampled_i
+
+    def _sample_sequential_topk(self, num_reqs: int, head_hidden: torch.Tensor) -> None:
+        """Apply the sequential Markov head only to top-k base-logit candidates.
+
+        Candidate selection is done once for all draft positions. At each
+        sequential step, the selected logits are corrected in place and every
+        other entry is set to ``-inf``. The normal dense sampling and rejection
+        paths then consume that truncated distribution unchanged.
+        """
+        assert self._draft_topk is not None
+        n_spec = self.num_speculative_steps
+        num_sample = num_reqs * n_spec
+        sample_hidden = head_hidden[self.sample_indices[:num_sample]]
+        base_logits = self.model.compute_draft_logits(sample_hidden)
+        base_logits = base_logits.view(num_reqs, n_spec, -1)
+        base_values, draft_indices = base_logits.topk(self._draft_topk, dim=-1)
+        # Reuse the dense backbone output as the normal sampler's input. Fill
+        # once for all positions, then scatter only the corrected candidates
+        # during the sequential loop.
+        base_logits.fill_(float("-inf"))
+        idx_map = self.sample_idx_mapping[:num_sample].view(num_reqs, n_spec)
+        sample_pos = self.sample_pos[:num_sample].view(num_reqs, n_spec)
+        prev = self.input_buffers.input_ids[self._anchor_idx[:num_reqs]]
+
+        for i in range(n_spec):
+            markov_embed = self.model.markov_embed(prev)
+            logits_i = self.model.apply_markov_bias_gathered(
+                markov_embed,
+                base_logits[:, i],
+                base_values[:, i],
+                draft_indices[:, i],
+            )
+            draft_sampled_i = self._sample_logits(
+                logits_i, idx_map[:, i], sample_pos[:, i], i
+            )
             self.draft_tokens[:num_reqs, i] = draft_sampled_i
             prev = draft_sampled_i