diff --git a/vllm/v1/worker/gpu_worker_states.py b/vllm/v1/worker/gpu_worker_states.py
index dac084ac307bd..8417ea6f0faf2 100644
--- a/vllm/v1/worker/gpu_worker_states.py
+++ b/vllm/v1/worker/gpu_worker_states.py
@@ -8,11 +8,9 @@ import numpy as np
 import torch
 import triton
 import triton.language as tl
-from typing_extensions import deprecated
 
 from vllm.lora.request import LoRARequest
-from vllm.multimodal.inputs import (MultiModalKwargsItem,
-                                    MultiModalKwargsItems, PlaceholderRange)
+from vllm.multimodal.inputs import MultiModalKwargsItem, PlaceholderRange
 from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingParams, SamplingType
 from vllm.v1.sample.logits_processor import LogitsProcessors
@@ -37,15 +35,6 @@ class RequestData:
 
     lora_request: Optional[LoRARequest] = None
 
-    # Temporary back-compatibility for plugins that define model runner
-    @property
-    @deprecated("`mm_inputs` is superseded by `mm_kwargs` and will be "
-                "removed in v0.13. Please use `mm_kwargs` instead.")
-    def mm_inputs(self) -> list[MultiModalKwargsItems]:
-        return [
-            MultiModalKwargsItems.from_seq([item]) for item in self.mm_kwargs
-        ]
-
 
 class RequestState:
 
@@ -81,26 +70,33 @@ class RequestState:
         self.req_data: dict[int, RequestData] = {}
         # TODO(woosuk): Because the token_ids tensor can be very big, we only
         # initialize it on CPU memory.
-        self.token_ids = self._make_param(
-            num_cols=self.max_model_len,
-            dtype=torch.int32,
-            cpu_only=True,
+        self.token_ids = np.zeros(
+            (self.max_num_cached_reqs, self.max_model_len),
+            dtype=np.int32,
         )
-        self.num_prompt_tokens = np.zeros(self.max_num_cached_reqs, dtype=np.int32)
+        self.num_prompt_tokens = np.zeros(self.max_num_cached_reqs,
+                                          dtype=np.int32)
         self.num_tokens = np.zeros(self.max_num_cached_reqs, dtype=np.int32)
-        self.num_computed_tokens = np.zeros(self.max_num_cached_reqs, dtype=np.int32)
+        self.num_computed_tokens = np.zeros(self.max_num_cached_reqs,
+                                            dtype=np.int32)
+
+        # Last sampled token ids.
+        self.last_sampled_token = torch.zeros(
+            self.max_num_cached_reqs,
+            dtype=torch.int32,
+            device=self.device,
+        )
 
         self.temperature = np.zeros(self.max_num_cached_reqs, dtype=np.float32)
-        self.greedy_req_indices: set[int] = set()
         self.top_p = np.zeros(self.max_num_cached_reqs, dtype=np.float32)
-        self.top_p_req_indices: set[int] = set()
         self.top_k = np.zeros(self.max_num_cached_reqs, dtype=np.int32)
-        self.top_k_req_indices: set[int] = set()
 
-        self.frequency_penalties = np.zeros(self.max_num_cached_reqs, dtype=np.float32)
-        self.presence_penalties = np.zeros(self.max_num_cached_reqs, dtype=np.float32)
-        self.repetition_penalties = np.zeros(self.max_num_cached_reqs, dtype=np.float32)
-        self.penalty_req_indices: set[int] = set()
+        self.frequency_penalties = np.zeros(self.max_num_cached_reqs,
+                                            dtype=np.float32)
+        self.presence_penalties = np.zeros(self.max_num_cached_reqs,
+                                           dtype=np.float32)
+        self.repetition_penalties = np.zeros(self.max_num_cached_reqs,
+                                             dtype=np.float32)
 
         self.generators: dict[int, torch.Generator] = {}
 
@@ -179,9 +175,9 @@ class RequestState:
         frequency_penalties = self.frequency_penalties[idx_mapping]
         presence_penalties = self.presence_penalties[idx_mapping]
         repetition_penalties = self.repetition_penalties[idx_mapping]
-        no_penalties = (np.all(frequency_penalties == 0.0) and
-                        np.all(presence_penalties == 0.0) and
-                        np.all(repetition_penalties == 1.0))
+        no_penalties = (np.all(frequency_penalties == 0.0)
+                        and np.all(presence_penalties == 0.0)
+                        and np.all(repetition_penalties == 1.0))
         if no_penalties:
             frequency_penalties = None
             presence_penalties = None
@@ -194,8 +190,7 @@ class RequestState:
         if self.generators:
             generators = {
                 req_idx: self.generators[req_idx]
-                for req_idx in idx_mapping
-                if req_idx in self.generators
+                for req_idx in idx_mapping if req_idx in self.generators
             }
         else:
             generators = {}