diff --git a/vllm/attention/backends/hpu_attn.py b/vllm/attention/backends/hpu_attn.py
index a8f4b09b67274..1197afd15d016 100644
--- a/vllm/attention/backends/hpu_attn.py
+++ b/vllm/attention/backends/hpu_attn.py
@@ -22,6 +22,10 @@
 
 class HPUAttentionBackend(AttentionBackend):
 
+    @staticmethod
+    def get_name() -> str:
+        return "hpu-attn"
+
     @staticmethod
     def get_impl_cls() -> Type["HPUAttentionImpl"]:
         return HPUAttentionImpl
diff --git a/vllm/executor/hpu_executor.py b/vllm/executor/hpu_executor.py
index 220e9eee87bb3..90e667e1965ec 100644
--- a/vllm/executor/hpu_executor.py
+++ b/vllm/executor/hpu_executor.py
@@ -48,9 +48,15 @@ def _create_worker(self,
                        local_rank: int = 0,
                        rank: int = 0,
                        distributed_init_method: Optional[str] = None):
+        if self.speculative_config is not None:
+            module_name = "vllm.spec_decode.spec_decode_worker"
+            class_name = "create_spec_worker"
+        else:
+            module_name = "vllm.worker.hpu_worker"
+            class_name = "HPUWorker"
         wrapper = WorkerWrapperBase(
-            worker_module_name="vllm.worker.hpu_worker",
-            worker_class_name="HPUWorker",
+            worker_module_name=module_name,
+            worker_class_name=class_name,
         )
         wrapper.init_worker(**self._get_worker_kwargs(local_rank, rank,
                                                       distributed_init_method))
diff --git a/vllm/spec_decode/hpu_draft_model_runner.py b/vllm/spec_decode/hpu_draft_model_runner.py
new file mode 100644
index 0000000000000..873b1970f6798
--- /dev/null
+++ b/vllm/spec_decode/hpu_draft_model_runner.py
@@ -0,0 +1,62 @@
+from typing import List, Optional
+
+import torch
+
+from vllm.logger import init_logger
+from vllm.model_executor.layers.sampler import SamplerOutput
+from vllm.sequence import IntermediateTensors
+from vllm.worker.hpu_model_runner import HPUModelRunner as ModelRunnerBaseCls
+from vllm.worker.hpu_model_runner import ModelInputForHPUWithSamplingMetadata
+
+logger = init_logger(__name__)
+
+# A flag to enable debug prints for the updated input tensors
+# before each step.
+debug_advance_input = False
+# A flag to allow GPU advance step for draft model runner.
+# Set to False for debugging.
+allow_gpu_advance_step = True
+
+
+class HPUTP1DraftModelRunner(ModelRunnerBaseCls):
+    """Specialized model runner for speculative decoding draft model.
+    Since the draft model always execute k forward passes consecutively to
+    generate k speculative tokens in a single speculative decoding step,
+    we could get rid of most CPU-GPU synchronization and data transfer
+    overheads by keeping model input and output tensors on GPU all the time.
+
+    TODOs:
+    1. Support TP > 1 (this requires some designs because we do not expect
+       any broadcasting inside execute_model).
+    """
+
+    def __init__(self, *args, **kwargs):
+        if kwargs.get("return_hidden_states"):
+            raise ValueError(
+                "return_hidden_states is not supported for TP1DraftModelRunner."
+            )
+
+        super().__init__(*args, **kwargs)
+
+        self.indices_of_seq_with_bonus_tokens = None
+
+    @torch.inference_mode()
+    def execute_model(
+        self,
+        model_input: ModelInputForHPUWithSamplingMetadata,
+        kv_caches: List[torch.Tensor],
+        previous_hidden_states: Optional[torch.Tensor] = None,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        num_steps: int = 1,
+    ) -> Optional[List[SamplerOutput]]:
+        if previous_hidden_states is not None:
+            _, block_size = model_input.input_tokens.shape
+            previous_hidden_states = previous_hidden_states.expand(
+                block_size, -1).unsqueeze(0)
+        return super().execute_model(
+            model_input=model_input,
+            kv_caches=kv_caches,
+            previous_hidden_states=previous_hidden_states,
+            intermediate_tensors=intermediate_tensors,
+            num_steps=num_steps,
+        )
\ No newline at end of file
diff --git a/vllm/spec_decode/spec_decode_worker.py b/vllm/spec_decode/spec_decode_worker.py
index 93419765e2c44..6231e431925a4 100644
--- a/vllm/spec_decode/spec_decode_worker.py
+++ b/vllm/spec_decode/spec_decode_worker.py
@@ -23,6 +23,10 @@
 
 if current_platform.is_cuda_alike():
     from vllm.spec_decode.draft_model_runner import TP1DraftModelRunner
+elif current_platform.is_hpu():
+    from vllm.spec_decode.hpu_draft_model_runner import (HPUTP1DraftModelRunner
+                                                         as
+                                                         TP1DraftModelRunner)
 
 from vllm.spec_decode.interfaces import (SpeculativeProposals,
                                          SpeculativeScorer, SpeculativeScores)
diff --git a/vllm/worker/hpu_model_runner.py b/vllm/worker/hpu_model_runner.py
index 7e9b2bd13b48a..154d64068803e 100644
--- a/vllm/worker/hpu_model_runner.py
+++ b/vllm/worker/hpu_model_runner.py
@@ -354,6 +354,14 @@ def compute_logits(self, *args, **kwargs):
     def sample(self, *args, **kwargs):
         return self.model.sample(*args, **kwargs)
 
+    def generate_proposals(self, *args, **kwargs):
+        return self.model.generate_proposals(*args, **kwargs)
+
+    # sampler property will be used by spec_decode_worker
+    @property
+    def sampler(self):
+        return self.model.sampler
+
 
 class PreparePromptMetadata(NamedTuple):
     input_tokens: torch.Tensor
@@ -514,6 +522,7 @@ class HPUModelRunnerBase(ModelRunnerBase[TModelInputForHPU]):
     def __init__(
         self,
         vllm_config: VllmConfig,
+        kv_cache_dtype: Optional[str] = "auto",
         is_driver_worker: bool = False,
         return_hidden_states: bool = False,
     ):
@@ -539,13 +548,18 @@ def __init__(
         self.pin_memory = is_pin_memory_available()
         self.kv_cache_dtype = self.cache_config.cache_dtype
 
+        num_attn_heads = self.model_config.get_num_attention_heads(
+            self.parallel_config)
+        needs_attn_backend = (num_attn_heads != 0
+                              or self.model_config.is_attention_free)
+
         self.attn_backend = get_attn_backend(
             self.model_config.get_head_size(),
             self.model_config.dtype,
             self.kv_cache_dtype,
             self.block_size,
             self.model_config.is_attention_free,
-        )
+        ) if needs_attn_backend else None
 
         # Lazy initialization
         self.lora_manager: LRUCacheWorkerLoRAManager = None
@@ -1879,6 +1893,7 @@ def execute_model(
         intermediate_tensors: Optional[IntermediateTensors] = None,
         num_steps: int = 1,
         warmup_mode=False,
+        previous_hidden_states: Optional[torch.Tensor] = None,
     ) -> Optional[Union[List[SamplerOutput], IntermediateTensors]]:
         if num_steps > 1:
             raise ValueError(
@@ -1922,6 +1937,9 @@ def execute_model(
             "lora_mask": lora_mask,
             **(model_input.multi_modal_kwargs or {}),
         }
+        if previous_hidden_states is not None:
+            execute_model_kwargs.update(
+                {"previous_hidden_states": previous_hidden_states})
         if htorch.utils.internal.is_lazy():
             execute_model_kwargs.update({"bypass_hpu_graphs": not use_graphs})
 
@@ -1987,6 +2005,11 @@ def execute_model(
                 real_batch_size=real_batch_size,
                 is_prompt=is_prompt)
             self.profiler.record_counter(self.event_start, counters)
+        if self.return_hidden_states:
+            # we only need to pass hidden states of most recent token
+            if model_input.is_prompt:
+                output.prefill_hidden_states = hidden_states
+            output.hidden_states = hidden_states
         return [output]
 
     def shutdown_inc(self):
diff --git a/vllm/worker/hpu_worker.py b/vllm/worker/hpu_worker.py
index 493f7a9fad098..1df078551cf67 100644
--- a/vllm/worker/hpu_worker.py
+++ b/vllm/worker/hpu_worker.py
@@ -22,7 +22,6 @@
 from vllm.sequence import ExecuteModelRequest
 from vllm.worker.cache_engine import CacheEngine
 from vllm.worker.hpu_model_runner import HPUModelRunner
-from vllm.worker.model_runner_base import ModelRunnerBase
 from vllm.worker.worker_base import (LocalOrDistributedWorkerBase, WorkerBase,
                                      WorkerInput)
 
@@ -44,7 +43,7 @@ def __init__(
         rank: int,
         distributed_init_method: str,
         is_driver_worker: bool = False,
-        model_runner_cls: Optional[Type[ModelRunnerBase]] = None,
+        model_runner_cls: Optional[Type[HPUModelRunner]] = None,
     ) -> None:
         WorkerBase.__init__(self, vllm_config=vllm_config)
         self.parallel_config.rank = rank
@@ -60,8 +59,28 @@ def __init__(
             from vllm.utils import init_cached_hf_modules
             init_cached_hf_modules()
 
-        self.model_runner: HPUModelRunner = HPUModelRunner(
-            vllm_config=vllm_config, is_driver_worker=is_driver_worker)
+        # Return hidden states from target model if the draft model is an
+        # mlp_speculator
+        speculative_config = self.speculative_config
+        model_config = self.model_config
+        speculative_args = {} if speculative_config is None \
+            or (speculative_config.draft_model_config.model ==
+                model_config.model) \
+            or (speculative_config.draft_model_config.hf_config.model_type
+                not in ["medusa", "mlp_speculator", "eagle"]) \
+                    else {"return_hidden_states": True}
+
+        ModelRunnerClass: Type[HPUModelRunner] = HPUModelRunner
+        if model_runner_cls is not None:
+            ModelRunnerClass = model_runner_cls
+        else:
+            ModelRunnerClass = HPUModelRunner
+        self.model_runner: HPUModelRunner = ModelRunnerClass(
+            vllm_config=vllm_config,
+            kv_cache_dtype=self.cache_config.cache_dtype,
+            is_driver_worker=is_driver_worker,
+            **speculative_args,
+        )
         # Uninitialized cache engine. Will be initialized by
         # initialize_cache.
         self.cache_engine: List[HPUCacheEngine]