diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py
index ba1428c42ee4..f5a9ada21899 100644
--- a/vllm/v1/worker/gpu_model_runner.py
+++ b/vllm/v1/worker/gpu_model_runner.py
@@ -379,6 +379,19 @@ def __init__(
         self.dcp_world_size = self.parallel_config.decode_context_parallel_size
         self.dcp_rank = 0 if self.dcp_world_size <= 1 else get_dcp_group().rank_in_group
         self.max_num_tokens = scheduler_config.max_num_batched_tokens
+        if self.speculative_config is not None and (
+            self.speculative_config.uses_draft_model()
+            or self.speculative_config.use_eagle()
+        ):
+            # When speculative decoding is enabled, additional slots are needed
+            # on top of the scheduler's allocation
+            multiplier = (
+                self.speculative_config.num_speculative_tokens
+                if self.speculative_config.parallel_drafting
+                else 1
+            )
+            self.max_num_tokens += multiplier * scheduler_config.max_num_seqs
+
         self.max_num_reqs = scheduler_config.max_num_seqs
 
         # Broadcast PP output for external_launcher (torchrun)
@@ -1662,10 +1675,7 @@ def _prepare_inputs(
 
         # Hot-Swap lora model
         if self.lora_config:
-            assert (
-                np.sum(num_sampled_tokens)
-                <= self.vllm_config.scheduler_config.max_num_batched_tokens
-            )
+            assert np.sum(num_sampled_tokens) <= self.max_num_tokens
             self.set_active_loras(
                 self.input_batch, num_scheduled_tokens, num_sampled_tokens
             )
@@ -4679,7 +4689,7 @@ def _dummy_run(
         # Set num_scheduled_tokens based on num_tokens and max_num_seqs
         # for dummy run with LoRA so that the num_reqs collectively
         # has num_tokens in total.
-        assert num_tokens <= self.scheduler_config.max_num_batched_tokens
+        assert num_tokens <= self.max_num_tokens
         max_num_reqs = self.scheduler_config.max_num_seqs
         if create_mixed_batch:
             assert not uniform_decode
@@ -6107,7 +6117,7 @@ def init_routed_experts_capturer(self):
             + 1
         ) * block_size
         routed_experts_capturer.init_buffer(
-            max_num_batched_tokens=self.scheduler_config.max_num_batched_tokens,
+            max_num_batched_tokens=self.max_num_tokens,
             max_num_kv_tokens=self.max_num_kv_tokens,
             vllm_config=self.vllm_config,
         )