diff --git a/vllm/v1/spec_decode/ngram_proposer.py b/vllm/v1/spec_decode/ngram_proposer.py
index 1273ca12c3600..75d0b3b835576 100644
--- a/vllm/v1/spec_decode/ngram_proposer.py
+++ b/vllm/v1/spec_decode/ngram_proposer.py
@@ -6,6 +6,7 @@ import numpy as np
 from numba import get_num_threads, jit, njit, prange, set_num_threads
 
 from vllm.config import VllmConfig
+from vllm.distributed import get_tp_group
 
 
 class NgramProposer:
@@ -137,33 +138,47 @@ class NgramProposer:
         token_ids_cpu: np.ndarray,
         spec_decode_unsupported_reqs: set,
     ) -> list[list[int]]:
-        # find which requests need ngram proposals
-        valid_ngram_requests = []
-        for i, sampled_ids in enumerate(sampled_token_ids):
-            num_sampled_ids = len(sampled_ids)
-            if not num_sampled_ids:
-                # Skip speculative decoding.
-                continue
+        # Only compute draft tokens on TP rank 0 and broadcast to other ranks.
+        # This ensures all TP ranks have identical draft tokens, which is
+        # required because numba parallel execution can produce different
+        # results across ranks due to non-determinism.
+        tp_group = get_tp_group()
+        if tp_group.is_first_rank:
+            # find which requests need ngram proposals
+            valid_ngram_requests = []
+            for i, sampled_ids in enumerate(sampled_token_ids):
+                num_sampled_ids = len(sampled_ids)
+                if not num_sampled_ids:
+                    # Skip speculative decoding.
+                    continue
 
-            # Skip requests that require sampling parameters that are not
-            # supported with speculative decoding.
-            req_id = req_ids[i]
-            if req_id in spec_decode_unsupported_reqs:
-                continue
+                # Skip requests that require sampling parameters that are not
+                # supported with speculative decoding.
+                req_id = req_ids[i]
+                if req_id in spec_decode_unsupported_reqs:
+                    continue
 
-            num_tokens = num_tokens_no_spec[i]
-            if num_tokens >= self.max_model_len:
-                # Skip requests that have already reached the max model length.
-                continue
+                num_tokens = num_tokens_no_spec[i]
+                if num_tokens >= self.max_model_len:
+                    # Skip requests that have already reached the max model length.
+                    continue
 
-            valid_ngram_requests.append(i)
+                valid_ngram_requests.append(i)
 
-        draft_token_ids = self.batch_propose(
-            len(sampled_token_ids),
-            valid_ngram_requests,
-            num_tokens_no_spec,
-            token_ids_cpu,
-        )
+            draft_token_ids = self.batch_propose(
+                len(sampled_token_ids),
+                valid_ngram_requests,
+                num_tokens_no_spec,
+                token_ids_cpu,
+            )
+        else:
+            draft_token_ids = None
+
+        # Broadcast draft tokens from rank 0 to all other ranks.
+        # Rank 0 always computes valid draft_token_ids, so broadcast
+        # will never return None.
+        draft_token_ids = tp_group.broadcast_object(draft_token_ids, src=0)
+        assert draft_token_ids is not None
 
         return draft_token_ids