diff --git a/vllm/v1/spec_decode/ngram_proposer.py b/vllm/v1/spec_decode/ngram_proposer.py
index 1273ca12c3600..52c40ee3375fc 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:
@@ -33,21 +34,17 @@ class NgramProposer:
         # Threshold of total number of tokens in the batch to enable
         # multi-threading in numba batch propose.
         self.num_tokens_threshold = 8192
-        tp_size = vllm_config.parallel_config.tensor_parallel_size
         cpu_count = os.cpu_count()
         # Max number of threads for numba parallel processing.
+        # Since draft tokens are computed only on rank 0 and broadcast to other
+        # ranks (for TP consistency), rank 0 can use all available threads.
         if cpu_count:
             # Divide by 2 to use physical cores
             # and not logical cores (hyper-threading).
             # Cap the number of threads to 8 to avoid using too many threads
             # since other components like frontend (incl tokenization)
             # and Structured Outputs also use multiple threads.
-            # TODO(ekagra-ranjan): bump up the cap from 1 to 8
-            # when TP parallelization for ngram is implemented.
-            self.num_numba_thread_available = min(1, (cpu_count // 2))
-            # Divide by tp_size to ensure each tensor parallel rank
-            # has some threads since all ranks will run this.
-            self.num_numba_thread_available //= tp_size
+            self.num_numba_thread_available = min(8, cpu_count // 2)
         else:
             self.num_numba_thread_available = 1
 
@@ -137,33 +134,47 @@ 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