diff --git a/python/sglang/srt/layers/attention/deepseek_v4_backend.py b/python/sglang/srt/layers/attention/deepseek_v4_backend.py
index 6f48fb817..883e38d28 100644
--- a/python/sglang/srt/layers/attention/deepseek_v4_backend.py
+++ b/python/sglang/srt/layers/attention/deepseek_v4_backend.py
@@ -53,6 +53,7 @@ from sglang.srt.layers.dp_attention import (
 )
 from sglang.srt.mem_cache.deepseek_v4_memory_pool import DeepSeekV4TokenToKVPool
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
+from sglang.srt.speculative.eagle_utils import per_step_draft_out_cache_loc
 from sglang.srt.speculative.spec_info import SpecInput
 from sglang.srt.utils import ceil_align
 
@@ -676,11 +677,22 @@ class DeepseekV4AttnBackend(
         max_seq_len = int(seq_lens_cpu.max().item())
 
         if forward_batch.forward_mode.is_decode_or_idle():
+            # DSv4 bakes this step's KV write target (c4/c128) into metadata,
+            # so slice the shared multi-step out_cache_loc now rather than at
+            # forward time.
+            out_cache_loc = forward_batch.out_cache_loc
+            if self.topk > 0 and self.speculative_num_steps > 1:
+                out_cache_loc = per_step_draft_out_cache_loc(
+                    out_cache_loc,
+                    forward_batch.batch_size,
+                    self.topk,
+                    self.speculative_num_steps,
+                )[self.speculative_step_id]
             metadata = self.init_forward_metadata_decode(
                 max_seq_len=max_seq_len,
                 req_pool_indices=req_pool_indices,
                 seq_lens=seq_lens,
-                out_cache_loc=forward_batch.out_cache_loc,
+                out_cache_loc=out_cache_loc,
             )
         elif forward_batch.forward_mode.is_target_verify():
             metadata = self.init_forward_metadata_target_verify(
diff --git a/python/sglang/srt/speculative/eagle_utils.py b/python/sglang/srt/speculative/eagle_utils.py
index 14f8fe340..a350bb2c4 100644
--- a/python/sglang/srt/speculative/eagle_utils.py
+++ b/python/sglang/srt/speculative/eagle_utils.py
@@ -23,6 +23,30 @@ if _is_cuda or _is_hip or _is_musa:
     )
 
 
+def per_step_draft_out_cache_loc(
+    out_cache_loc: torch.Tensor,
+    batch_size: int,
+    topk: int,
+    num_steps: int,
+) -> torch.Tensor:
+    """Per-step slice of the multi-step EAGLE draft out_cache_loc buffer.
+
+    Single source of truth for the layout shared by EagleWorkerV2.draft_forward
+    (per-step write target) and DeepseekV4AttnBackend (per-step compression
+    write target baked into metadata).
+    """
+    expected = batch_size * topk * num_steps
+    assert out_cache_loc.shape[0] == expected, (
+        f"out_cache_loc.shape[0]={out_cache_loc.shape[0]} != "
+        f"batch_size * topk * num_steps = {batch_size}*{topk}*{num_steps}={expected}"
+    )
+    return (
+        out_cache_loc.view(batch_size, topk, num_steps)
+        .permute(2, 0, 1)
+        .reshape(num_steps, -1)
+    )
+
+
 def apply_eagle_prefill_input_rotation(
     batch: ScheduleBatch, next_token_ids: torch.Tensor
 ) -> None:
diff --git a/python/sglang/srt/speculative/eagle_worker_v2.py b/python/sglang/srt/speculative/eagle_worker_v2.py
index a3d14af91..18a947e1a 100644
--- a/python/sglang/srt/speculative/eagle_worker_v2.py
+++ b/python/sglang/srt/speculative/eagle_worker_v2.py
@@ -53,7 +53,11 @@ from sglang.srt.speculative.eagle_info_v2 import (
     fill_accepted_out_cache_loc,
     fill_bonus_tokens,
 )
-from sglang.srt.speculative.eagle_utils import TreeMaskMode, build_tree_kernel_efficient
+from sglang.srt.speculative.eagle_utils import (
+    TreeMaskMode,
+    build_tree_kernel_efficient,
+    per_step_draft_out_cache_loc,
+)
 from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
 from sglang.srt.speculative.spec_utils import (
     draft_tp_context,
@@ -438,11 +442,11 @@ class EagleDraftWorker(BaseDraftWorker):
         if self.hot_token_id is not None:
             topk_index = self.hot_token_id[topk_index]
 
-        out_cache_loc = out_cache_loc.reshape(
-            forward_batch.batch_size, self.topk, self.speculative_num_steps
-        )
-        out_cache_loc = out_cache_loc.permute((2, 0, 1)).reshape(
-            self.speculative_num_steps, -1
+        out_cache_loc = per_step_draft_out_cache_loc(
+            out_cache_loc,
+            forward_batch.batch_size,
+            self.topk,
+            self.speculative_num_steps,
         )
 
         # Return values