diff --git a/vllm/lora/ops/ipex_ops/__init__.py b/vllm/lora/ops/ipex_ops/__init__.py
new file mode 100644
index 0000000000000..5daa432493b19
--- /dev/null
+++ b/vllm/lora/ops/ipex_ops/__init__.py
@@ -0,0 +1,7 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from vllm.lora.ops.ipex_ops.lora_ops import (bgmv_expand, bgmv_expand_slice,
+                                             bgmv_shrink)
+
+__all__ = ["bgmv_expand", "bgmv_expand_slice", "bgmv_shrink"]
diff --git a/vllm/lora/ops/ipex_ops/lora_ops.py b/vllm/lora/ops/ipex_ops/lora_ops.py
new file mode 100644
index 0000000000000..7590c868ecb67
--- /dev/null
+++ b/vllm/lora/ops/ipex_ops/lora_ops.py
@@ -0,0 +1,44 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+try:
+    import intel_extension_for_pytorch as ipex
+except ImportError as e:
+    raise e
+
+
+def bgmv_shrink(inputs: torch.Tensor,
+                lora_a_weights: torch.Tensor,
+                output_tensor: torch.Tensor,
+                lora_indices_tensor: torch.Tensor,
+                scaling: float = 1.0) -> None:
+
+    ipex.llm.functional.bgmv_shrink(inputs, lora_a_weights, output_tensor,
+                                    lora_indices_tensor, scaling)
+
+
+def bgmv_expand(inputs: torch.Tensor,
+                lora_b_weights: torch.Tensor,
+                output_tensor: torch.Tensor,
+                lora_indices_tensor: torch.Tensor,
+                add_inputs: bool = True) -> None:
+    ipex.llm.functional.bgmv_expand(inputs, lora_b_weights, output_tensor,
+                                    lora_indices_tensor, add_inputs)
+
+
+def bgmv_expand_slice(inputs: torch.Tensor,
+                      lora_b_weights: torch.Tensor,
+                      output_tensor: torch.Tensor,
+                      lora_indices_tensor: torch.Tensor,
+                      slice_offset: int,
+                      slice_size: int,
+                      add_inputs: bool = True) -> None:
+    ipex.llm.functional.bgmv_expand_slice(inputs, lora_b_weights,
+                                          output_tensor, lora_indices_tensor,
+                                          slice_offset, slice_size, add_inputs)
diff --git a/vllm/lora/punica_wrapper/punica_xpu.py b/vllm/lora/punica_wrapper/punica_xpu.py
new file mode 100644
index 0000000000000..572e39e0eced0
--- /dev/null
+++ b/vllm/lora/punica_wrapper/punica_xpu.py
@@ -0,0 +1,269 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Based on:
+Chen, L., Ye, Z., Wu, Y., Zhuo, D., Ceze, L., & Krishnamurthy, A. (2023). 
+Punica: Multi-Tenant LoRA Serving. 
+https://arxiv.org/abs/2310.18547
+"""
+
+from typing import Optional, Union, final
+
+import torch
+
+from vllm.lora.layers import LoRAMapping
+from vllm.lora.ops.ipex_ops import bgmv_expand, bgmv_expand_slice, bgmv_shrink
+
+from .punica_base import PunicaWrapperBase
+
+
+@final
+class PunicaWrapperXPU(PunicaWrapperBase):
+    """
+    PunicaWrapperXPU is designed to manage and provide metadata for the punica
+    kernel. The main function is to maintain the state information for 
+    Multi-LoRA, and to provide the interface for the punica ipex kernel.
+    """
+
+    def __init__(self, max_num_batched_tokens: int, max_batches: int,
+                 device: Union[torch.device, str], **kwargs):
+        PunicaWrapperBase.__init__(self, max_num_batched_tokens, max_batches,
+                                   device)
+        torch._dynamo.mark_dynamic(self._token_lora_indices, 0)
+        torch._dynamo.mark_dynamic(self._embeddings_indices, 1)
+        torch._dynamo.mark_dynamic(self._sampler_indices_padded, 0)
+
+    def update_metadata(self, mapping: LoRAMapping,
+                        lora_index_to_id: list[Optional[int]], max_loras: int,
+                        vocab_size: int, extra_vocab_size: int, **kwargs):
+
+        self.is_prefill = mapping.is_prefill
+        self._update_base_metadata(mapping, lora_index_to_id, max_loras,
+                                   vocab_size, extra_vocab_size)
+
+    def _get_token_lora_indices(self, x: torch.Tensor) -> torch.IntTensor:
+        return torch.narrow(self._token_lora_indices, 0, 0, x.size(0))
+
+    def _apply_shrink(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        w_t_all: torch.Tensor,
+        scale: float,
+    ):
+        bgmv_shrink(x, w_t_all, y, self._get_token_lora_indices(x), scale)
+
+    def _apply_expand(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        w_t_all: torch.Tensor,
+        y_offset: int,
+        y_slice_size: int,
+        add_inputs: bool,
+    ):
+        token_lora_indices = self._get_token_lora_indices(x)
+        bgmv_expand_slice(x, w_t_all, y, token_lora_indices, y_offset,
+                          y_slice_size, add_inputs)
+
+    def add_shrink(self, y: torch.Tensor, x: torch.Tensor,
+                   lora_a_stacked: tuple[torch.Tensor,
+                                         ...], scale: float, **kwargs):
+        """
+        Performs GEMM  for multiple slices of lora_a.
+            
+        Semantics:
+        for i in range(len(lora_a_stacked)):
+            y[i] += (x @ lora_a_stacked[i]) * scale
+        
+        Args:
+            y (torch.Tensor): Output tensors
+            x (torch.Tensor): Input tensor
+            lora_a_stacked (tuple[torch.Tensor, ...]): lora_a's weights
+            scale (float): Scaling factor for the operation
+        """
+
+        x = x.view(-1, x.shape[-1])
+        for slice_idx in range(len(lora_a_stacked)):
+            self._apply_shrink(y[slice_idx], x, lora_a_stacked[slice_idx],
+                               scale)
+
+    def add_expand(self,
+                   y: torch.Tensor,
+                   x: torch.Tensor,
+                   lora_b_stacked: tuple[torch.Tensor, ...],
+                   lora_bias_stacked: Optional[tuple[torch.Tensor, ...]],
+                   output_slices: tuple[int, ...],
+                   offset_start: int = 0,
+                   add_inputs=True,
+                   **kwargs) -> None:
+        """
+        Performs GEMM and bias addition for multiple slices of lora_b.
+      
+        Semantics:
+            for i in range(len(lora_b_stacked)):
+                slice = output_slices[i]
+                y[:, offset:offset+slice] += x[i] @ lora_b_stacked[i] + 
+                    lora_bias_stacked[i] 
+                offset += slice
+            
+        Args:
+            y (torch.Tensor): Output tensor.
+            x (torch.Tensor): Input tensors
+            lora_b_stacked (tuple[torch.Tensor, ...]): lora_b's weight
+            lora_bias_stacked (Optional[tuple[torch.Tensor, ...]]): 
+                bias's weight
+            output_slices (tuple[int, ...]): Every slice's size
+            add_inputs (bool): Defaults to True.
+        """
+        y_org = y
+        y = y.view(-1, y.shape[-1])
+        if lora_bias_stacked is not None:
+            token_lora_indices = self._get_token_lora_indices(y)
+            self._apply_bias(token_lora_indices, y, output_slices,
+                             lora_bias_stacked)
+
+        assert x.ndim == 3
+        assert x.size(0) == len(output_slices)
+
+        # TODO fuse these kernels
+        for slice_idx in range(len(lora_b_stacked)):
+            self._apply_expand(
+                y,
+                x[slice_idx],
+                lora_b_stacked[slice_idx],
+                offset_start,
+                output_slices[slice_idx],
+                add_inputs=add_inputs,
+            )
+            offset_start += output_slices[slice_idx]
+        y.view_as(y_org)
+
+    def add_lora_embedding(self,
+                           y: torch.Tensor,
+                           x: torch.Tensor,
+                           lora_b_stacked: torch.Tensor,
+                           add_inputs: bool = True,
+                           **kwargs) -> None:
+        """
+        Applies lora  specifically for VocabParallelEmbeddingWithLoRA.
+
+        Semantics:
+            y += x @ lora_b_stacked
+
+        Args:
+            y (torch.Tensor): Output tensor.
+            x (torch.Tensor): Input tensor.
+            lora_b_stacked (torch.Tensor): lora_b's weights.
+            add_inputs (bool): Default to True.
+        """
+        token_lora_indices = self._get_token_lora_indices(x)
+        bgmv_expand(x, lora_b_stacked, y, token_lora_indices, add_inputs)
+
+    def add_lora_linear(self,
+                        y: torch.Tensor,
+                        x: torch.Tensor,
+                        lora_a_stacked: tuple[torch.Tensor, ...],
+                        lora_b_stacked: tuple[torch.Tensor, ...],
+                        lora_bias_stacked: Optional[tuple[torch.Tensor, ...]],
+                        scale: float,
+                        output_slices: tuple[int, ...],
+                        *,
+                        buffer: Optional[torch.Tensor] = None,
+                        **kwargs) -> None:
+        """
+        Applicable to linear-related lora.
+
+        Semantics:
+            for i in range(len(lora_a_stacked)):
+                y[i] += (
+                    x[i].unsqueeze(0)
+                    @ lora_a_stacked[indices[i], layer_idx, :, :]
+                    @ lora_b_stacked[indices[i], layer_idx, :, :]
+                    * scale
+                    ).squeeze(0)+lora_bias_stacked[i]
+
+        Args:
+            y (torch.Tensor): Output tensor. Will be changed in-place.
+            x (torch.Tensor): Input tensor
+            lora_a_stacked (tuple[torch.Tensor, ...]): lora_a's weight.
+            lora_b_stacked (tuple[torch.Tensor, ...]): lora_b's weight.
+            lora_bias_stacked (Optional[tuple[torch.Tensor, ...]]): lora's bias.
+            scale (float): Scaling factor.
+            output_slices (tuple[int, ...]): Every slice's size.
+            buffer (Optional[torch.Tensor]): Defaults to None.
+        """
+
+        assert len(lora_a_stacked) == len(lora_b_stacked) == len(output_slices)
+        if lora_bias_stacked is not None:
+            assert len(lora_bias_stacked) == len(output_slices)
+            token_lora_indices = self._get_token_lora_indices(y)
+            y = self._apply_bias(token_lora_indices, y, output_slices,
+                                 lora_bias_stacked)
+
+        if buffer is None:
+            r = lora_b_stacked[0].size(-1)
+            # We set the buffer to be float32 by default, refer to:
+            # https://github.com/triton-lang/triton/issues/1387
+            buffer = torch.zeros(  # type: ignore
+                (len(output_slices), x.size(0), r),
+                dtype=torch.float32,
+                device=x.device,
+            )
+        self.add_shrink(
+            buffer,  # type: ignore
+            x,
+            lora_a_stacked,
+            scale,
+            **kwargs)
+        self.add_expand(
+            y,
+            buffer,  # type: ignore
+            lora_b_stacked,
+            None,
+            output_slices,
+            add_inputs=True,
+            **kwargs)
+
+    def add_lora_logits(self,
+                        y: torch.Tensor,
+                        x: torch.Tensor,
+                        lora_a_stacked: torch.Tensor,
+                        lora_b_stacked: torch.Tensor,
+                        scale,
+                        *,
+                        buffer: Optional[torch.Tensor] = None,
+                        **kwargs) -> None:
+        """
+        Applies lora  specifically for LogitsProcessorWithLoRA.
+        
+        Semantics:
+            buffer = (x @ lora_a_stacked) * scale
+            y += buffer @ lora_b_stacked
+
+        Args:
+            y (torch.Tensor): Output tensor.
+            x (torch.Tensor): Input tensor.
+            lora_a_stacked (torch.Tensor): lora_a's weights.
+            lora_b_stacked (torch.Tensor): lora_b's weights.
+            scale (float): Scaling factor.
+            buffer (Optional[torch.Tensor]): Default to None.
+        """
+        y_org = y
+        y = y.view(-1, y.shape[-1])
+        x = x.view(-1, x.shape[-1])
+        r = lora_b_stacked.size(-1)
+        if buffer is None:
+            # We set the buffer to be float32 by default, refer to:
+            # https://github.com/triton-lang/triton/issues/1387
+            buffer = torch.zeros((x.size(0), r),
+                                 dtype=torch.float32,
+                                 device=x.device)
+
+        bgmv_shrink(x, lora_a_stacked, buffer, self.sampler_indices, scale)
+        bgmv_expand(buffer,
+                    lora_b_stacked,
+                    y,
+                    self.sampler_indices,
+                    add_inputs=True)
+        return y.view_as(y_org)
diff --git a/vllm/platforms/xpu.py b/vllm/platforms/xpu.py
index c4530c1dfaa3f..1d0bb3654929b 100644
--- a/vllm/platforms/xpu.py
+++ b/vllm/platforms/xpu.py
@@ -67,7 +67,7 @@ class XPUPlatform(Platform):
 
     @classmethod
     def get_punica_wrapper(cls) -> str:
-        return "vllm.lora.punica_wrapper.punica_gpu.PunicaWrapperGPU"
+        return "vllm.lora.punica_wrapper.punica_xpu.PunicaWrapperXPU"
 
     @classmethod
     def get_device_total_memory(cls, device_id: int = 0) -> int: