diff --git a/python/sglang/srt/disaggregation/prefill.py b/python/sglang/srt/disaggregation/prefill.py
index 9298130f8..7e7b63cec 100644
--- a/python/sglang/srt/disaggregation/prefill.py
+++ b/python/sglang/srt/disaggregation/prefill.py
@@ -933,6 +933,35 @@ class SchedulerDisaggregationPrefillMixin:
                     logits_output.input_token_logprobs.tolist()
                 )
 
+        # Batch the D2H copies of the EAGLE spec outputs: one gathered copy per
+        # tensor for all finishing requests instead of one synchronous device
+        # sync per request here (hidden_states) and per request inside
+        # MetadataBuffers.set_buf (topk_p/topk_index, which were stored as GPU
+        # row views and copied into the CPU metadata buffers one by one).
+        spec_cpu_row = None
+        if self.spec_algorithm.is_eagle() and batch.spec_info is not None:
+            finished_idx = [i for i, r in enumerate(batch.reqs) if r.is_chunked <= 0]
+            if finished_idx:
+                spec_cpu_row = {i: k for k, i in enumerate(finished_idx)}
+                if len(finished_idx) == len(batch.reqs):
+                    gather = lambda t: t
+                else:
+                    gather_idx = torch.tensor(
+                        finished_idx,
+                        dtype=torch.int64,
+                        device=batch.spec_info.hidden_states.device,
+                    )
+                    gather = lambda t: t.index_select(0, gather_idx)
+                # .to(copy=True) detaches from spec_info even when the batch is
+                # already on CPU (set_buf reads these synchronously below).
+                spec_topk_p_cpu = gather(batch.spec_info.topk_p).to("cpu", copy=True)
+                spec_topk_index_cpu = gather(batch.spec_info.topk_index).to(
+                    "cpu", copy=True
+                )
+                spec_hidden_cpu = gather(batch.spec_info.hidden_states).to(
+                    "cpu", copy=True
+                )
+
         for i, (req, next_token_id) in enumerate(
             zip(batch.reqs, next_token_ids, strict=True)
         ):
@@ -943,12 +972,11 @@ class SchedulerDisaggregationPrefillMixin:
                 req.output_ids.append(next_token_id)
                 self.tree_cache.cache_unfinished_req(req)  # update the tree and lock
                 self.disagg_prefill_inflight_queue.append(req)
-                if self.spec_algorithm.is_eagle() and batch.spec_info is not None:
-                    req.output_topk_p = batch.spec_info.topk_p[i]
-                    req.output_topk_index = batch.spec_info.topk_index[i]
-                    req.hidden_states_tensor = (
-                        batch.spec_info.hidden_states[i].cpu().clone()
-                    )
+                if spec_cpu_row is not None:
+                    k = spec_cpu_row[i]
+                    req.output_topk_p = spec_topk_p_cpu[k]
+                    req.output_topk_index = spec_topk_index_cpu[k]
+                    req.hidden_states_tensor = spec_hidden_cpu[k]
                 else:
                     req.hidden_states_tensor = None
                 if req.return_logprob: