diff --git a/benchmarks/kernels/benchmark_lora.py b/benchmarks/kernels/benchmark_lora.py
index ecde8fbaa15b8..b7bc840508169 100644
--- a/benchmarks/kernels/benchmark_lora.py
+++ b/benchmarks/kernels/benchmark_lora.py
@@ -23,6 +23,9 @@
 from vllm.lora.ops.triton_ops.sgmv_expand import sgmv_expand
 from vllm.lora.ops.triton_ops.sgmv_shrink import sgmv_shrink
 from vllm.lora.ops.triton_ops.utils import _LORA_A_PTR_DICT, _LORA_B_PTR_DICT
+from vllm.lora.v1.ops.triton_ops.v1_expand import v1_expand
+from vllm.lora.v1.ops.triton_ops.v1_shrink import v1_shrink
+from vllm.lora.v1.punica_wrapper.punica_gpu_v1 import V1KernelMeta
 from vllm.utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
@@ -172,6 +175,8 @@ class OpType(Enum):
     SGMV_EXPAND = auto()
     BGMV_EXPAND = auto()
     BGMV_EXPAND_SLICE = auto()
+    V1_SHRINK = auto()
+    V1_EXPAND = auto()
 
     @staticmethod
     def from_str(s: str) -> "OpType":
@@ -185,28 +190,43 @@ def from_str(s: str) -> "OpType":
             return OpType.BGMV_EXPAND
         if s.lower() == "bgmv_expand_slice":
             return OpType.BGMV_EXPAND_SLICE
+        if s.lower() == "v1_shrink":
+            return OpType.V1_SHRINK
+        if s.lower() == "v1_expand":
+            return OpType.V1_EXPAND
         raise ValueError(f"Unrecognized str {s} to convert to OpType")
 
     def is_shrink_fn(self) -> bool:
-        return self in [OpType.SGMV_SHRINK, OpType.BGMV_SHRINK]
+        return self in [
+            OpType.SGMV_SHRINK, OpType.BGMV_SHRINK, OpType.V1_SHRINK
+        ]
 
     def is_expand_fn(self) -> bool:
-        return self in [OpType.SGMV_EXPAND, OpType.BGMV_EXPAND]
+        return self in [
+            OpType.SGMV_EXPAND, OpType.BGMV_EXPAND, OpType.V1_EXPAND
+        ]
 
     def is_prefill_op(self) -> bool:
-        return self in [OpType.SGMV_SHRINK, OpType.SGMV_EXPAND]
+        return self in [
+            OpType.SGMV_SHRINK, OpType.SGMV_EXPAND, OpType.V1_SHRINK,
+            OpType.V1_EXPAND
+        ]
 
     def is_decode_op(self) -> bool:
         return self in [
-            OpType.BGMV_SHRINK, OpType.BGMV_EXPAND, OpType.BGMV_EXPAND_SLICE
+            OpType.BGMV_SHRINK, OpType.BGMV_EXPAND, OpType.BGMV_EXPAND_SLICE,
+            OpType.V1_SHRINK, OpType.V1_EXPAND
         ]
 
     def is_expand_slice_fn(self) -> bool:
         return self in [OpType.BGMV_EXPAND_SLICE]
 
     def num_slices(self) -> List[int]:
-        if self in [OpType.SGMV_EXPAND, OpType.SGMV_SHRINK]:
-            # SGMV kernels supports slices
+        if self in [
+                OpType.SGMV_EXPAND, OpType.SGMV_SHRINK, OpType.V1_SHRINK,
+                OpType.V1_EXPAND
+        ]:
+            # SGMV kernels and v1 kernels supports slices
             return [1, 2, 3]
         if self in [OpType.BGMV_SHRINK, OpType.BGMV_EXPAND]:
             return [1]
@@ -251,11 +271,13 @@ def matmul_shapes(
         m, k, n = self.mkn(batch_size, seq_length, hidden_size, lora_rank)
 
         b_shape = (num_loras, n, k)  # col-major
-        if self == OpType.SGMV_SHRINK:
-            # SGMV shrink supports num_slices inherently in the kernel
+        if self in [OpType.SGMV_SHRINK, OpType.V1_SHRINK]:
+            # SGMV shrink and V1 shrink kernels support num_slices inherently
+            # in the kernel.
             return ((m, k), b_shape, (num_slices, m, n))
-        if self == OpType.SGMV_EXPAND:
-            # SGMV expand supports num_slices inherently in the kernel
+        if self in [OpType.SGMV_EXPAND, OpType.V1_EXPAND]:
+            # SGMV expand and V1 expand kernels support num_slices inherently
+            # in the kernel
             return ((num_slices, m, k), b_shape, (m, n * num_slices))
         if self == OpType.BGMV_SHRINK:
             return ((m, k), b_shape, (m, n))
@@ -282,25 +304,30 @@ def emulate_bgmv_expand_slice(kwargs_list: List[Dict[str, Any]]):
             return bgmv_expand
         if self == OpType.BGMV_EXPAND_SLICE:
             return emulate_bgmv_expand_slice
+        if self == OpType.V1_SHRINK:
+            return v1_shrink
+        if self == OpType.V1_EXPAND:
+            return v1_expand
+
         raise ValueError(f"Unrecognized optype {self}")
 
     def run_ref_group_gemm(self, output: torch.Tensor, input: torch.Tensor,
                            lora_weights: List[torch.Tensor],
                            **kwargs) -> Callable:
-        """Each benchmark operation expected the input, lora_weights and outputs
+        """Each benchmark operation expects the input, lora_weights and outputs
            in a slightly different format. Refer to self.matmul_shapes().
            run_ref_group_gemm accounts for those differences in executing a
            reference group gemm for correctness testing.
         """
         w_dtype = lora_weights[0].dtype
         num_slices = len(lora_weights)
-        if self == OpType.SGMV_SHRINK:
+        if self in [OpType.SGMV_SHRINK, OpType.V1_SHRINK]:
             for slice_idx in range(num_slices):
                 ref_group_gemm(ref_out=output[slice_idx, :],
                                input=input,
                                lora_weights=lora_weights[slice_idx],
                                **kwargs)
-        if self == OpType.SGMV_EXPAND:
+        elif self in [OpType.SGMV_EXPAND, OpType.V1_EXPAND]:
             hidden_size = lora_weights[0].shape[1]
             for slice_idx in range(num_slices):
                 slice_offset = slice_idx * hidden_size
@@ -309,19 +336,19 @@ def run_ref_group_gemm(self, output: torch.Tensor, input: torch.Tensor,
                     input=input[slice_idx].clone().to(dtype=w_dtype),
                     lora_weights=lora_weights[slice_idx],
                     **kwargs)
-        if self == OpType.BGMV_SHRINK:
+        elif self == OpType.BGMV_SHRINK:
             assert num_slices == 1
             ref_group_gemm(ref_out=output,
                            input=input,
                            lora_weights=lora_weights[0],
                            **kwargs)
-        if self == OpType.BGMV_EXPAND:
+        elif self == OpType.BGMV_EXPAND:
             assert num_slices == 1
             ref_group_gemm(ref_out=output,
                            input=input.clone().to(dtype=w_dtype),
                            lora_weights=lora_weights[0],
                            **kwargs)
-        if self == OpType.BGMV_EXPAND_SLICE:
+        elif self == OpType.BGMV_EXPAND_SLICE:
             hidden_size = lora_weights[0].shape[1]
             for slice_idx in range(num_slices):
                 slice_offset = slice_idx * hidden_size
@@ -330,7 +357,8 @@ def run_ref_group_gemm(self, output: torch.Tensor, input: torch.Tensor,
                     input=input[slice_idx].clone().to(dtype=w_dtype),
                     lora_weights=lora_weights[slice_idx],
                     **kwargs)
-        raise ValueError(f"Unrecognized optype {self}")
+        else:
+            raise ValueError(f"Unrecognized optype {self}")
 
 
 @dataclass
@@ -391,6 +419,8 @@ class BenchmarkTensors:
     seq_start_loc: torch.Tensor
     prompt_lora_mapping: torch.Tensor
     token_lora_mapping: torch.Tensor
+    # v1 kernel metadata
+    v1_kernel_meta: Optional[V1KernelMeta] = None
 
     def io_types(self) -> str:
         return (f"{dtype_to_str(self.input.dtype)}x"
@@ -433,10 +463,19 @@ def make(ctx: BenchmarkContext,
             total_tokens, ctx.batch_size, prompt_lora_indices_tensor,
             seq_len_tensor, "cpu")
 
+        v1_kernel_meta = None
+        if op_type in [OpType.V1_SHRINK, OpType.V1_EXPAND]:
+            v1_kernel_meta = V1KernelMeta.make(
+                max_loras=ctx.num_loras,
+                max_num_tokens=token_lora_indices_tensor.size(0),
+                device="cpu")
+            v1_kernel_meta.prepare_tensors(
+                token_lora_mapping=token_lora_indices_tensor)
+
         return BenchmarkTensors(input_tensor, lora_weights, output_tensor,
                                 seq_len_tensor, seq_start_loc_tensor,
                                 prompt_lora_indices_tensor,
-                                token_lora_indices_tensor)
+                                token_lora_indices_tensor, v1_kernel_meta)
 
     def sanity_check(self) -> None:
         """
@@ -469,6 +508,13 @@ def to_device(tensor: torch.Tensor):
         for i in range(len(self.lora_weights_lst)):
             self.lora_weights_lst[i] = to_device(self.lora_weights_lst[i])
 
+        # v1 meta
+        if self.v1_kernel_meta:
+            for field_name in V1KernelMeta.__dataclass_fields__:
+                field = getattr(self.v1_kernel_meta, field_name)
+                assert isinstance(field, torch.Tensor)
+                setattr(self.v1_kernel_meta, field_name, to_device(field))
+
     def metadata(self) -> Tuple[int, int, int]:
         """
         Return num_seqs, num_tokens and max_seq_len
@@ -668,6 +714,78 @@ def as_bgmv_expand_slice_kwargs(self, add_inputs: bool) -> Dict[str, Any]:
             })
         return {'kwargs_list': kwargs_list}
 
+    def as_v1_shrink_kwargs(self) -> Dict[str, Any]:
+        assert self.v1_kernel_meta is not None
+        self.sanity_check()
+        self.to_device(self.input.device)
+
+        _, num_tokens, _, num_slices = self.metadata()
+
+        # Sanity check matrix shapes.
+        i_shape, lw_shape, o_shape = self.input.shape, self.lora_weights_lst[
+            0].shape, self.output.shape
+        # Expected input shape [num_tokens, hidden_size]
+        assert len(i_shape) == 2
+        assert i_shape[0] == num_tokens
+        hidden_size = i_shape[1]
+        # Expected lora weight shape [num_loras, lora_rank, hidden_size]
+        assert len(lw_shape) == 3
+        assert lw_shape[2] == hidden_size
+        lora_rank = lw_shape[1]
+        # Expected output shape [num_slices, num_tokens, lora_rank]
+        assert len(o_shape) == 3
+        assert o_shape == (num_slices, num_tokens, lora_rank)
+
+        return {
+            'inputs': self.input,
+            'lora_a_weights': self.lora_weights_lst,
+            'output_tensor': self.output,
+            'token_lora_mapping': self.v1_kernel_meta.token_lora_mapping,
+            'token_indices_sorted_by_lora_ids':
+            self.v1_kernel_meta.token_indices_sorted_by_lora_ids,
+            'num_tokens_per_lora': self.v1_kernel_meta.num_tokens_per_lora,
+            'lora_token_start_loc': self.v1_kernel_meta.lora_token_start_loc,
+            'lora_ids': self.v1_kernel_meta.active_lora_ids,
+            'scaling': 1.0,
+        }
+
+    def as_v1_expand_kwargs(self, add_inputs: bool) -> Dict[str, Any]:
+        assert self.v1_kernel_meta is not None
+        self.sanity_check()
+        self.to_device(self.input.device)
+
+        _, num_tokens, _, num_slices = self.metadata()
+
+        # Sanity check matrix shapes.
+        i_shape, lw_shape, o_shape = self.input.shape, self.lora_weights_lst[
+            0].shape, self.output.shape
+        # Expected input shape : [num_slices, num_tokens, lora_rank]
+        assert len(i_shape) == 3
+        assert i_shape[0] == num_slices
+        assert i_shape[1] == num_tokens
+        lora_rank = i_shape[2]
+        # Expected lora weight shape : [num_lora, hidden_size, lora_rank]
+        assert len(lw_shape) == 3
+        assert lw_shape[2] == lora_rank
+        hidden_size = lw_shape[1]
+        # Expected output shape : [num_tokens, hidden_size * num_slices]
+        assert len(o_shape) == 2
+        assert o_shape == (num_tokens, hidden_size * num_slices)
+
+        return {
+            'inputs': self.input,
+            'lora_b_weights': self.lora_weights_lst,
+            'output_tensor': self.output,
+            'token_lora_mapping': self.v1_kernel_meta.token_lora_mapping,
+            'token_indices_sorted_by_lora_ids':
+            self.v1_kernel_meta.token_indices_sorted_by_lora_ids,
+            'num_tokens_per_lora': self.v1_kernel_meta.num_tokens_per_lora,
+            'lora_token_start_loc': self.v1_kernel_meta.lora_token_start_loc,
+            'lora_ids': self.v1_kernel_meta.active_lora_ids,
+            'offset_start': 0,
+            'add_inputs': add_inputs,
+        }
+
     def bench_fn_kwargs(self,
                         op_type: OpType,
                         add_inputs: Optional[bool] = None) -> Dict[str, Any]:
@@ -686,6 +804,10 @@ def bench_fn_kwargs(self,
             return self.as_bgmv_expand_kwargs(add_inputs)
         if op_type == OpType.BGMV_EXPAND_SLICE:
             return self.as_bgmv_expand_slice_kwargs(add_inputs)
+        if op_type == OpType.V1_SHRINK:
+            return self.as_v1_shrink_kwargs()
+        if op_type == OpType.V1_EXPAND:
+            return self.as_v1_expand_kwargs(add_inputs)
         raise ValueError(f"Unrecognized optype {self}")
 
     def test_correctness(self, op_type: OpType,
diff --git a/tests/lora/test_punica_ops.py b/tests/lora/test_punica_ops.py
index 032e20470bcd3..03cafdaec111d 100644
--- a/tests/lora/test_punica_ops.py
+++ b/tests/lora/test_punica_ops.py
@@ -6,10 +6,12 @@
 import torch
 
 import vllm.lora.ops.triton_ops  # noqa: F401
+import vllm.lora.v1.ops.triton_ops  # noqa: F401
 from vllm.lora.ops.torch_ops import (bgmv_expand, bgmv_expand_slice,
                                      bgmv_shrink, sgmv_expand,
                                      sgmv_expand_slice, sgmv_shrink)
 from vllm.lora.ops.triton_ops.utils import _LORA_A_PTR_DICT, _LORA_B_PTR_DICT
+from vllm.lora.v1.punica_wrapper.punica_gpu_v1 import V1KernelMeta
 from vllm.platforms import current_platform
 
 from .utils import (PunicaTensors, assert_close, generate_data,
@@ -313,6 +315,111 @@ def check_bgmv_expand_slice(batches: int, num_loras: int, rank: int,
     assert_close(data.our_out_tensor, data.ref_out_tensor)
 
 
+def check_v1_shrink(batches: int, num_loras: int, rank: int, hidden_size: int,
+                    nslices: int, dtype: torch.dtype, device: str,
+                    seq_length: int, scaling: float):
+    """
+    Compare vllm.v1_shrink against a reference implementation.
+    """
+    data: PunicaTensors = generate_data_for_nslices(
+        batches,
+        hidden_size,
+        num_loras,
+        rank,
+        seq_length,
+        nslices,
+        dtype,
+        "shrink",
+        device,
+    )
+    max_seq_length, token_nums = data.meta()
+
+    # Setup metadata information for reference sgmv kernels
+    sgmv_meta_args = (data.b_seq_start_loc, data.seq_len_tensor,
+                      data.prompt_lora_mapping, batches, max_seq_length,
+                      token_nums)
+
+    # Setup metadata information for the kernel.
+    v1_meta = V1KernelMeta.make(max_loras=num_loras,
+                                max_num_tokens=token_nums,
+                                device='cuda')
+    v1_meta.reset()
+    v1_meta.prepare_tensors(data.token_lora_mapping)
+
+    # Preventing cache error pointer.
+    with _dict_lock:
+        _LORA_A_PTR_DICT.clear()
+        torch.ops.vllm.v1_shrink(
+            data.inputs_tensor,
+            data.lora_weights,
+            data.our_out_tensor,
+            *v1_meta.meta_args(num_tokens=token_nums),
+            scaling,
+        )
+
+    sgmv_shrink_for_nslices(
+        nslices,
+        data.inputs_tensor,
+        data.lora_weights,
+        data.ref_out_tensor,
+        *sgmv_meta_args,
+        scaling,
+    )
+
+    assert_close(data.our_out_tensor, data.ref_out_tensor)
+
+
+def check_v1_expand(batches: int, num_loras: int, rank: int, hidden_size: int,
+                    nslices: int, dtype: torch.dtype, device: str,
+                    seq_length: int, add_inputs: bool):
+    """
+    Compare vllm.v1_expand against a reference implementation.
+    """
+    data: PunicaTensors = generate_data_for_nslices(
+        batches,
+        hidden_size,
+        num_loras,
+        rank,
+        seq_length,
+        nslices,
+        dtype,
+        "expand",
+        device,
+    )
+    max_seq_length, token_nums = data.meta()
+
+    # Setup metadata information for reference sgmv kernels
+    sgmv_meta_args = (data.b_seq_start_loc, data.seq_len_tensor,
+                      data.prompt_lora_mapping, batches, max_seq_length,
+                      token_nums)
+
+    # Setup metadata information for the kernel.
+    v1_meta = V1KernelMeta.make(max_loras=num_loras,
+                                max_num_tokens=token_nums,
+                                device='cuda')
+    v1_meta.reset()
+    v1_meta.prepare_tensors(data.token_lora_mapping)
+
+    with _dict_lock:
+        _LORA_B_PTR_DICT.clear()
+        torch.ops.vllm.v1_expand(data.inputs_tensor,
+                                 data.lora_weights,
+                                 data.our_out_tensor,
+                                 *v1_meta.meta_args(num_tokens=token_nums),
+                                 offset_start=0,
+                                 add_inputs=add_inputs)
+
+    sgmv_expand_for_nslices(nslices,
+                            hidden_size,
+                            data.inputs_tensor,
+                            data.lora_weights,
+                            data.ref_out_tensor,
+                            *sgmv_meta_args,
+                            add_inputs=add_inputs)
+
+    assert_close(data.our_out_tensor, data.ref_out_tensor)
+
+
 # Tests
 # We test the punica kernels along 2 verticals mainly.
 # 1. Variations in hidden_dim size
@@ -650,3 +757,82 @@ def test_punica_bgmv_expand_nslices_hidden_size(batches: int, num_loras: int,
                             dtype=dtype,
                             device=device,
                             add_inputs=True)
+
+
+@pytest.mark.parametrize("batches", test_params['batches'])
+@pytest.mark.parametrize("num_loras", test_params['num_loras'])
+@pytest.mark.parametrize("rank", test_params['max_ranks'])
+@pytest.mark.parametrize("hidden_size", test_params['hidden_sizes'])
+@pytest.mark.parametrize("nslices", [1, 2, 3])
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("device", DEVICES)
+@pytest.mark.parametrize("seed", SEED)
+@pytest.mark.parametrize("seq_length", [1, 128])
+@pytest.mark.parametrize("op_type", ["shrink", "expand"])
+def test_v1_kernels(batches: int, num_loras: int, rank: int, hidden_size: int,
+                    nslices: int, dtype: torch.dtype, device: str, seed: int,
+                    seq_length: int, op_type: str):
+
+    torch.set_default_device(device)
+    current_platform.seed_everything(seed)
+
+    if op_type == "shrink":
+        check_v1_shrink(batches=batches,
+                        num_loras=num_loras,
+                        rank=rank,
+                        hidden_size=hidden_size,
+                        nslices=nslices,
+                        dtype=dtype,
+                        device=device,
+                        seq_length=seq_length,
+                        scaling=0.5)
+    else:
+        check_v1_expand(batches=batches,
+                        num_loras=num_loras,
+                        rank=rank,
+                        hidden_size=hidden_size,
+                        nslices=nslices,
+                        dtype=dtype,
+                        device=device,
+                        seq_length=seq_length,
+                        add_inputs=True)
+
+
+@pytest.mark.parametrize("batches", hs_test_params['batches'])
+@pytest.mark.parametrize("num_loras", hs_test_params['num_loras'])
+@pytest.mark.parametrize("rank", hs_test_params['max_ranks'])
+@pytest.mark.parametrize("hidden_size", hs_test_params['hidden_sizes'])
+@pytest.mark.parametrize("nslices", [1, 2, 3])
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("device", DEVICES)
+@pytest.mark.parametrize("seed", SEED)
+@pytest.mark.parametrize("seq_length", [1, 13])
+@pytest.mark.parametrize("op_type", ["shrink", "expand"])
+def test_v1_kernels_hidden_size(batches: int, num_loras: int, rank: int,
+                                hidden_size: int, nslices: int,
+                                dtype: torch.dtype, device: str, seed: int,
+                                seq_length: int, op_type: str):
+
+    torch.set_default_device(device)
+    current_platform.seed_everything(seed)
+
+    if op_type == "shrink":
+        check_v1_shrink(batches=batches,
+                        num_loras=num_loras,
+                        rank=rank,
+                        hidden_size=hidden_size,
+                        nslices=nslices,
+                        dtype=dtype,
+                        device=device,
+                        seq_length=seq_length,
+                        scaling=0.5)
+    else:
+        check_v1_expand(batches=batches,
+                        num_loras=num_loras,
+                        rank=rank,
+                        hidden_size=hidden_size,
+                        nslices=nslices,
+                        dtype=dtype,
+                        device=device,
+                        seq_length=seq_length,
+                        add_inputs=True)
diff --git a/vllm/lora/models.py b/vllm/lora/models.py
index ef77fd4b74cec..d7e5e77f94f85 100644
--- a/vllm/lora/models.py
+++ b/vllm/lora/models.py
@@ -324,9 +324,11 @@ def __init__(
         self.lora_index_to_id: List[Optional[int]] = [None] * self.lora_slots
         self.vocab_size = vocab_size
         self.long_lora_context: Optional[LongContextLoRAContext] = None
-        self.punica_wrapper = get_punica_wrapper(max_num_batched_tokens,
-                                                 max_batches=self.max_num_seqs,
-                                                 device=self.device)
+        self.punica_wrapper = get_punica_wrapper(
+            max_num_batched_tokens,
+            max_batches=self.max_num_seqs,
+            device=self.device,
+            max_loras=self.lora_config.max_loras)
         # Scaling factor -> offset to the sin_cos_cache to it.
         # Used for long context lora.
         self.scaling_factor_to_offset: Dict[float, int] = {}
diff --git a/vllm/lora/ops/triton_ops/kernel_utils.py b/vllm/lora/ops/triton_ops/kernel_utils.py
new file mode 100644
index 0000000000000..d7b95d76027bb
--- /dev/null
+++ b/vllm/lora/ops/triton_ops/kernel_utils.py
@@ -0,0 +1,244 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Utilities for Punica kernel construction.
+"""
+import triton
+import triton.language as tl
+
+
+@triton.jit
+def mm_k(a_ptr, b_ptr, ak_stride, bk_stride, offset_k, K: tl.constexpr,
+         BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
+         EVEN_K: tl.constexpr, SPLIT_K: tl.constexpr, CAST_TYPE: tl.constexpr,
+         b_dtype: tl.constexpr):
+    """
+    Given a_ptr and b_ptr, that identify the rows of A (m x k) and columns of
+    B (k x n), iterate, through the K dimension to compute the partial/complete
+    matrix block product.
+    If SPLIT_K == 1, the output m x n product is complete.
+    If SPLIT_K > 1, the thread block computes partial outputs. The partial
+    outputs are then atomically summed in the caller code. 
+    Args:
+        a_ptr: Array of pointers, identifying rows of A 
+        b_ptr: Array of pointers, identifying columns of B
+        ak_stride: K dimension stride of the A matrix
+        bk_stride: K dimension stride of the B matrix
+        K: Length of the K dimension
+        BLOCK_M: M dimension of the output block m x n
+        BLOCK_N: N dimension of the output block m x n
+        BLOCK_K: K dimension atom
+        EVEN_K: True iff the blocks of A and B can be loaded without any
+          masking.
+        SPLIT_K: Parameter signifying parallelism in the K dimension. 
+        CAST_TYPE: if True, cast the values from the A matrix to the B
+          matrix dtype.
+        b_dtype: datatype of the B matrix
+    """
+    accumulator = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
+    for k in range(tl.cdiv(K, BLOCK_K * SPLIT_K)):
+        if EVEN_K:
+            tiled_a = tl.load(a_ptr)
+            tiled_b = tl.load(b_ptr)
+        else:
+            tiled_a = tl.load(a_ptr,
+                              mask=offset_k[None, :]
+                              < K - k * (BLOCK_K * SPLIT_K),
+                              other=0)
+            tiled_b = tl.load(b_ptr,
+                              mask=offset_k[:, None]
+                              < K - k * (BLOCK_K * SPLIT_K),
+                              other=0)
+        if CAST_TYPE:
+            tiled_a = tiled_a.to(b_dtype)
+        accumulator += tl.dot(
+            tiled_a,
+            tiled_b,
+        )
+        a_ptr += BLOCK_K * SPLIT_K * ak_stride
+        b_ptr += BLOCK_K * SPLIT_K * bk_stride
+    return accumulator
+
+
+@triton.jit
+def do_expand_kernel(
+    pid_n,
+    lora_index,
+    slice_id,
+    input_ptr,
+    lora_ptr,
+    out_ptr,
+    N,
+    K,
+    M_LEN,
+    ram,  # array identifying the rows of Input ptr to operate on
+    slice_start_loc,
+    # input ptr strides
+    input_d0_stride,
+    input_d1_stride,
+    input_d2_stride,
+    # lora ptr strides
+    ls_d0_ptr,
+    ls_d1_ptr,
+    ls_d2_ptr,
+    # out ptr strides
+    output_d0_stride,
+    output_d1_stride,
+    # constants
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+    BLOCK_K: tl.constexpr,
+    SAME_STRIDE: tl.constexpr,
+    SLICE_NUM: tl.constexpr,
+    EVEN_K: tl.constexpr,
+    CAST_TYPE: tl.constexpr,
+    ADD_INPUTS: tl.constexpr,
+):
+    """
+    Given an array of integers that identifies the rows of A, ram,
+    a lora index that identifies which LoRA to use from lora_ptr, lora_index,
+    a slice_id that identifies the input/output slice,
+    compute the matrix product and store in the appropriate output location.
+
+    Given that this is an expand kernel, we don't perform any split-K reduction
+    as the K dimension is assumed to be small.
+    """
+
+    # ls_d*_ptr can be either an integer or a pointer
+    if SAME_STRIDE:
+        # integer
+        cur_lora_d0_stride = ls_d0_ptr
+        cur_lora_d1_stride = ls_d1_ptr
+        cur_lora_d2_stride = ls_d2_ptr
+    else:
+        # pointer
+        cur_lora_d0_stride = tl.load(ls_d0_ptr + slice_id)
+        cur_lora_d1_stride = tl.load(ls_d1_ptr + slice_id)
+        cur_lora_d2_stride = tl.load(ls_d2_ptr + slice_id)
+
+    # Identify the input_ptr and lora_ptr from slice_id.
+    if SLICE_NUM == 1:
+        cur_input_ptr = input_ptr
+        cur_lora_ptr = lora_ptr
+    else:
+        cur_input_ptr = input_ptr + slice_id * input_d0_stride
+        cur_lora_ptr = tl.load(lora_ptr + slice_id).to(
+            tl.pointer_type(out_ptr.dtype.element_ty))
+
+    # Identify the column indices of B to process.
+    offset_n = tl.arange(0, BLOCK_N) + pid_n * BLOCK_N
+    rbn = tl.max_contiguous(tl.multiple_of(offset_n % N, BLOCK_N), BLOCK_N)
+
+    # Identify the row pointers of A and column pointers of B
+    offset_k = tl.arange(0, BLOCK_K)
+    a_ptr = (cur_input_ptr + ram[:, None] * input_d1_stride +
+             offset_k[None, :] * input_d2_stride, )
+    b_ptr = (cur_lora_ptr + cur_lora_d0_stride * lora_index +
+             offset_k[:, None] * cur_lora_d2_stride +
+             rbn[None, :] * cur_lora_d1_stride)
+
+    # Compute the block matrix product.
+    SPLIT_K = 1
+    accumulator = mm_k(a_ptr, b_ptr, input_d2_stride, cur_lora_d2_stride,
+                       offset_k, K, BLOCK_M, BLOCK_N, BLOCK_K, EVEN_K, SPLIT_K,
+                       CAST_TYPE, cur_lora_ptr.dtype.element_ty)
+
+    tiled_c = accumulator.to(cur_lora_ptr.dtype.element_ty)
+    if SLICE_NUM == 1:
+        cur_slice_start = slice_start_loc
+    else:
+        cur_slice_start = tl.load(slice_start_loc + slice_id)
+
+    # Identify the C output pointers to store the results of the accumulator.
+    offset_cn = tl.arange(0, BLOCK_N) + pid_n * BLOCK_N + cur_slice_start
+    offset_cm = tl.arange(0, BLOCK_M)
+    c_ptr = (out_ptr + ram[:, None] * output_d0_stride +
+             offset_cn[None, :] * output_d1_stride)
+    c_mask = (offset_cm[:, None] < M_LEN) & (offset_cn[None, :]
+                                             < (cur_slice_start + N))
+
+    if ADD_INPUTS:
+        tiled_out = tl.load(c_ptr, mask=c_mask)
+        tiled_c += tiled_out
+    tl.store(c_ptr, tiled_c, mask=c_mask)
+
+
+@triton.jit
+def do_shrink_kernel(
+    pid_n,
+    pid_sk,
+    slice_id,
+    lora_index,
+    input_ptr,
+    lora_ptr,
+    out_ptr,
+    N,
+    K,
+    M_LEN,
+    ram,
+    # input strides
+    input_d0_stride,
+    input_d1_stride,
+    # lora strides
+    lora_d0_stride,
+    lora_d1_stride,
+    lora_d2_stride,
+    # output strides
+    output_d0_stride,
+    output_d1_stride,
+    output_d2_stride,
+    scaling,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+    BLOCK_K: tl.constexpr,
+    EVEN_K: tl.constexpr,
+    SPLIT_K: tl.constexpr,
+    SLICE_NUM: tl.constexpr,
+):
+    """
+    Given an array of integers that identifies the rows of A, ram,
+    a lora index that identifies which LoRA to use from lora_ptr, lora_index,
+    a slice_id that identifies the input/output slice, compute the
+    matrix product and store in the appropriate output location.
+    """
+
+    # Identify the lora_ptr from slice_id.
+    if SLICE_NUM == 1:
+        # current lora ptr
+        cur_lora_ptr = lora_ptr
+    else:
+        # current lora ptr
+        cur_lora_ptr = tl.load(lora_ptr + slice_id).to(
+            tl.pointer_type(input_ptr.dtype.element_ty))
+
+    # Identify the column indices of B to process.
+    offset_n = tl.arange(0, BLOCK_N) + pid_n * BLOCK_N
+    rbn = tl.max_contiguous(tl.multiple_of(offset_n % N, BLOCK_N), BLOCK_N)
+
+    # Identify the row pointers of A and column pointers of B
+    offset_k = pid_sk * BLOCK_K + tl.arange(0, BLOCK_K)
+    a_ptr = (input_ptr + ram[:, None] * input_d0_stride +
+             offset_k[None, :] * input_d1_stride)
+    b_ptr = (cur_lora_ptr + lora_d0_stride * lora_index +
+             rbn[None, :] * lora_d1_stride +
+             offset_k[:, None] * lora_d2_stride)
+
+    # Compute partial/complete block matrix product.
+    accumulator = mm_k(a_ptr, b_ptr, input_d1_stride, lora_d2_stride, offset_k,
+                       K, BLOCK_M, BLOCK_N, BLOCK_K, EVEN_K, SPLIT_K, False,
+                       cur_lora_ptr.dtype.element_ty)
+
+    # Identify the C output pointers to store the results of the accumulator.
+    offset_cn = tl.arange(0, BLOCK_N) + pid_n * BLOCK_N
+    offset_cm = tl.arange(0, BLOCK_M)
+    cur_out_ptr = (out_ptr if SLICE_NUM == 1 else out_ptr +
+                   slice_id * output_d0_stride)
+    c_ptr = cur_out_ptr + ram[:, None] * output_d1_stride + offset_cn[
+        None, :] * output_d2_stride
+    c_mask = (offset_cm[:, None] < M_LEN) & (offset_cn[None, :] < N)
+
+    accumulator *= scaling
+    # handles write-back with reduction-splitting
+    if SPLIT_K == 1:
+        tl.store(c_ptr, accumulator, mask=c_mask)
+    else:
+        tl.atomic_add(c_ptr, accumulator, mask=c_mask)
diff --git a/vllm/lora/ops/triton_ops/sgmv_expand.py b/vllm/lora/ops/triton_ops/sgmv_expand.py
index a8e71cacfe5a2..4eeda9876d84e 100644
--- a/vllm/lora/ops/triton_ops/sgmv_expand.py
+++ b/vllm/lora/ops/triton_ops/sgmv_expand.py
@@ -14,6 +14,7 @@
 
 from vllm.utils import direct_register_custom_op
 
+from .kernel_utils import do_expand_kernel
 from .utils import _get_lora_b_ptr
 
 
@@ -63,86 +64,56 @@ def _sgmv_expand_kernel(
     curr_N = N if SAME_STRIDE else tl.load(output_hs_ptr + slice_id)
     pid_m = pid // cta_n_num
     pid_n = pid % cta_n_num
-    M = tl.load(seq_lens + cur_batch)
-    if pid_m * BLOCK_M > M:
+
+    M_LEN = tl.load(seq_lens + cur_batch)
+    if pid_m * BLOCK_M >= M_LEN:
         return
-    if pid_n * BLOCK_N > curr_N:
+    if pid_n * BLOCK_N >= curr_N:
         return
     lora_index = tl.load(lora_indices + cur_batch)
     if lora_index == -1:
         return
 
-    cur_seq_start = tl.load(b_seq_start_loc + cur_batch)
-    offset_m = tl.arange(0, BLOCK_M) + pid_m * BLOCK_M
-    offset_n = tl.arange(0, BLOCK_N) + pid_n * BLOCK_N
-    offset_k = tl.arange(0, BLOCK_K)
-    ram = tl.max_contiguous(tl.multiple_of(offset_m % M, BLOCK_M), BLOCK_M)
-    rbn = tl.max_contiguous(tl.multiple_of(offset_n % curr_N, BLOCK_N),
-                            BLOCK_N)
-    # ls_d*_ptr can be either an integer or a pointer
-    if SAME_STRIDE:
-        # integer
-        cur_lora_d0_stride = ls_d0_ptr
-        cur_lora_d1_stride = ls_d1_ptr
-        cur_lora_d2_stride = ls_d2_ptr
-    else:
-        # pointer
-        cur_lora_d0_stride = tl.load(ls_d0_ptr + slice_id)
-        cur_lora_d1_stride = tl.load(ls_d1_ptr + slice_id)
-        cur_lora_d2_stride = tl.load(ls_d2_ptr + slice_id)
-    if SLICE_NUM == 1:
-        cur_input_ptr = input_ptr
-        cur_lora_ptr = lora_ptr
-
-    else:
-        cur_input_ptr = input_ptr + slice_id * input_d0_stride
-        cur_lora_ptr = tl.load(lora_ptr + slice_id).to(
-            tl.pointer_type(out_ptr.dtype.element_ty))
-
-    a_ptr = (cur_input_ptr + cur_seq_start * input_d1_stride +
-             ram[:, None] * input_d1_stride +
-             offset_k[None, :] * input_d2_stride, )
-    b_ptr = (cur_lora_ptr + cur_lora_d0_stride * lora_index +
-             offset_k[:, None] * cur_lora_d2_stride +
-             rbn[None, :] * cur_lora_d1_stride)
-    accumulator = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
-    for k in range(tl.cdiv(K, BLOCK_K)):
-        if EVEN_K:
-            tiled_a = tl.load(a_ptr)
-            tiled_b = tl.load(b_ptr)
-        else:
-            tiled_a = tl.load(a_ptr,
-                              mask=offset_k[None, :] < K - k * BLOCK_K,
-                              other=0)
-            tiled_b = tl.load(b_ptr,
-                              mask=offset_k[:, None] < K - k * BLOCK_K,
-                              other=0)
-        if CAST_TYPE:
-            tiled_a = tiled_a.to(cur_lora_ptr.dtype.element_ty)
-        accumulator += tl.dot(
-            tiled_a,
-            tiled_b,
-        )
-        a_ptr += BLOCK_K * input_d2_stride
-        b_ptr += BLOCK_K * cur_lora_d2_stride
-
-    tiled_c = accumulator.to(cur_lora_ptr.dtype.element_ty)
-    if SLICE_NUM == 1:
-        cur_slice_start = slice_start_loc
-    else:
-        cur_slice_start = tl.load(slice_start_loc + slice_id)
-
-    offset_cm = cur_seq_start + tl.arange(0, BLOCK_M) + pid_m * BLOCK_M
-    offset_cn = tl.arange(0, BLOCK_N) + pid_n * BLOCK_N + cur_slice_start
-    c_ptr = (out_ptr + offset_cm[:, None] * output_d0_stride +
-             offset_cn[None, :] * output_d1_stride)
-    M = tl.load(seq_lens + cur_batch)
-    c_mask = (offset_cm[:, None] < (cur_seq_start + M)) & (
-        offset_cn[None, :] < (cur_slice_start + curr_N))
-    if ADD_INPUTS:
-        tiled_out = tl.load(c_ptr, mask=c_mask)
-        tiled_c += tiled_out
-    tl.store(c_ptr, tiled_c, mask=c_mask)
+    M_OFFSET = tl.load(b_seq_start_loc + cur_batch)
+
+    CTA_M_LEN = min(BLOCK_M, M_LEN - (pid_m * BLOCK_M))
+    CTA_M_OFFSET = M_OFFSET + (pid_m * BLOCK_M)
+    offset_m = tl.arange(0, BLOCK_M)
+    ram = CTA_M_OFFSET + tl.max_contiguous(
+        tl.multiple_of(offset_m % CTA_M_LEN, BLOCK_M), BLOCK_M)
+    do_expand_kernel(
+        pid_n,
+        lora_index,
+        slice_id,
+        input_ptr,
+        lora_ptr,
+        out_ptr,
+        curr_N,
+        K,
+        CTA_M_LEN,
+        ram,  # array identifying the rows of Input ptr to operate on
+        slice_start_loc,
+        # input ptr strides
+        input_d0_stride,
+        input_d1_stride,
+        input_d2_stride,
+        # lora ptr strides
+        ls_d0_ptr,
+        ls_d1_ptr,
+        ls_d2_ptr,
+        # out ptr strides
+        output_d0_stride,
+        output_d1_stride,
+        # constants
+        BLOCK_M,
+        BLOCK_N,
+        BLOCK_K,
+        SAME_STRIDE,
+        SLICE_NUM,
+        EVEN_K,
+        CAST_TYPE,
+        ADD_INPUTS,
+    )
 
 
 @torch.inference_mode()
diff --git a/vllm/lora/ops/triton_ops/sgmv_shrink.py b/vllm/lora/ops/triton_ops/sgmv_shrink.py
index 8b26583c11c14..a74770878b995 100644
--- a/vllm/lora/ops/triton_ops/sgmv_shrink.py
+++ b/vllm/lora/ops/triton_ops/sgmv_shrink.py
@@ -14,6 +14,7 @@
 
 from vllm.utils import direct_register_custom_op
 
+from .kernel_utils import do_shrink_kernel
 from .utils import _get_lora_a_ptr
 
 
@@ -61,68 +62,50 @@ def _sgmv_shrink_kernel(
         slice_id = pid_mix // SPLIT_K
         pid_sk = pid_mix % SPLIT_K
 
-    M = tl.load(seq_lens + cur_batch)
-    if pid_m * BLOCK_M > M:
+    M_LEN = tl.load(seq_lens + cur_batch)
+    if pid_m * BLOCK_M >= M_LEN:
         return
     lora_index = tl.load(lora_indices + cur_batch)
     if lora_index == -1:
         return
-    cur_seq_start = tl.load(b_seq_start_loc + cur_batch)
-    offset_m = tl.arange(0, BLOCK_M) + pid_m * BLOCK_M
-    offset_n = tl.arange(0, BLOCK_N) + pid_n * BLOCK_N
-    offset_k = pid_sk * BLOCK_K + tl.arange(0, BLOCK_K)
-
-    ram = tl.max_contiguous(tl.multiple_of(offset_m % M, BLOCK_M), BLOCK_M)
-    rbn = tl.max_contiguous(tl.multiple_of(offset_n % N, BLOCK_N), BLOCK_N)
-    # input ptr
-    a_ptr = (input_ptr + cur_seq_start * input_d0_stride +
-             ram[:, None] * input_d0_stride +
-             offset_k[None, :] * input_d1_stride)
-
-    if SLICE_NUM == 1:
-        # current lora ptr
-        cur_lora_ptr = lora_ptr
-    else:
-        # current lora ptr
-        cur_lora_ptr = tl.load(lora_ptr + slice_id).to(
-            tl.pointer_type(input_ptr.dtype.element_ty))
-
-    b_ptr = (cur_lora_ptr + lora_d0_stride * lora_index +
-             rbn[None, :] * lora_d1_stride +
-             offset_k[:, None] * lora_d2_stride)
-
-    accumulator = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
-    for k in range(0, tl.cdiv(K, BLOCK_K * SPLIT_K)):
-        if EVEN_K:
-            tiled_a = tl.load(a_ptr)
-            tiled_b = tl.load(b_ptr)
-        else:
-            k_remaining = K - k * (BLOCK_K * SPLIT_K)
-            tiled_a = tl.load(a_ptr,
-                              mask=offset_k[None, :] < k_remaining,
-                              other=0.0)
-            tiled_b = tl.load(b_ptr,
-                              mask=offset_k[:, None] < k_remaining,
-                              other=0.0)
-        accumulator += tl.dot(tiled_a, tiled_b)
-
-        a_ptr += BLOCK_K * SPLIT_K * input_d1_stride
-        b_ptr += BLOCK_K * SPLIT_K * lora_d2_stride
-    offset_cm = cur_seq_start + tl.arange(0, BLOCK_M) + pid_m * BLOCK_M
-
-    offset_cn = tl.arange(0, BLOCK_N) + pid_n * BLOCK_N
-    cur_out_ptr = (out_ptr if SLICE_NUM == 1 else out_ptr +
-                   slice_id * output_d0_stride)
-    c_ptr = cur_out_ptr + offset_cm[:, None] * output_d1_stride + offset_cn[
-        None, :] * output_d2_stride
-    c_mask = (offset_cm[:, None] < (cur_seq_start + M)) & (offset_cn[None, :]
-                                                           < N)
-    accumulator *= scaling
-    # handles write-back with reduction-splitting
-    if SPLIT_K == 1:
-        tl.store(c_ptr, accumulator, mask=c_mask)
-    else:
-        tl.atomic_add(c_ptr, accumulator, mask=c_mask)
+    M_OFFSET = tl.load(b_seq_start_loc + cur_batch)
+
+    CTA_M_LEN = min(BLOCK_M, M_LEN - (pid_m * BLOCK_M))
+    CTA_M_OFFSET = M_OFFSET + (pid_m * BLOCK_M)
+    offset_m = tl.arange(0, BLOCK_M)
+    ram = CTA_M_OFFSET + tl.max_contiguous(
+        tl.multiple_of(offset_m % CTA_M_LEN, BLOCK_M), BLOCK_M)
+
+    do_shrink_kernel(
+        pid_n,
+        pid_sk,
+        slice_id,
+        lora_index,
+        input_ptr,
+        lora_ptr,
+        out_ptr,
+        N,
+        K,
+        CTA_M_LEN,
+        ram,
+        # input strides
+        input_d0_stride,
+        input_d1_stride,
+        # lora strides
+        lora_d0_stride,
+        lora_d1_stride,
+        lora_d2_stride,
+        # output strides
+        output_d0_stride,
+        output_d1_stride,
+        output_d2_stride,
+        scaling,
+        BLOCK_M,
+        BLOCK_N,
+        BLOCK_K,
+        EVEN_K,
+        SPLIT_K,
+        SLICE_NUM)
 
 
 @torch.inference_mode()
diff --git a/vllm/lora/punica_wrapper/punica_base.py b/vllm/lora/punica_wrapper/punica_base.py
index 1a2282ae9accd..abbac52bac080 100644
--- a/vllm/lora/punica_wrapper/punica_base.py
+++ b/vllm/lora/punica_wrapper/punica_base.py
@@ -147,7 +147,7 @@ def __init__(self, max_num_batched_tokens: int, max_batches: int,
                                               dtype=torch.long,
                                               device=device)
 
-        # 5 is the number of indicies tensors.
+        # 5 is the number of indices tensors.
         # base_indices, sampler_indices, sampler_indices_padded,
         # embeddings_indices,long_lora_indices
         self.indices_len: List[Optional[int]] = [None] * 5
@@ -168,7 +168,7 @@ def __init__(self, max_num_batched_tokens: int, max_batches: int,
         self.is_prefill = False
         self.no_lora = False
 
-    def _update_base_metadata(
+    def update_base_metadata(
         self,
         mapping: "LoRAMapping",
         lora_index_to_id: List[Optional[int]],
@@ -329,9 +329,9 @@ def update_metadata(
             long_lora_context: Optional["LongContextLoRAContext"] = None,
             **kwargs):
 
-        self._update_base_metadata(mapping, lora_index_to_id, max_loras,
-                                   vocab_size, extra_vocab_size,
-                                   long_lora_context)
+        self.update_base_metadata(mapping, lora_index_to_id, max_loras,
+                                  vocab_size, extra_vocab_size,
+                                  long_lora_context)
         if mapping.is_prefill:
             # Update metadata required for prefill-related operators.
             self._update_prefill_metada(self.token_lora_indices)
diff --git a/vllm/lora/v1/ops/triton_ops/__init__.py b/vllm/lora/v1/ops/triton_ops/__init__.py
new file mode 100644
index 0000000000000..f5ca3aa42ea1d
--- /dev/null
+++ b/vllm/lora/v1/ops/triton_ops/__init__.py
@@ -0,0 +1,9 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from vllm.lora.v1.ops.triton_ops.v1_expand import v1_expand
+from vllm.lora.v1.ops.triton_ops.v1_shrink import v1_shrink
+
+__all__ = [
+    "v1_shrink",
+    "v1_expand",
+]
\ No newline at end of file
diff --git a/vllm/lora/v1/ops/triton_ops/configs/NVIDIA_A100_SXM4_80GB_EXPAND_FALSE.json b/vllm/lora/v1/ops/triton_ops/configs/NVIDIA_A100_SXM4_80GB_EXPAND_FALSE.json
new file mode 100644
index 0000000000000..e22680d308620
--- /dev/null
+++ b/vllm/lora/v1/ops/triton_ops/configs/NVIDIA_A100_SXM4_80GB_EXPAND_FALSE.json
@@ -0,0 +1,4643 @@
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+}
\ No newline at end of file
diff --git a/vllm/lora/v1/ops/triton_ops/configs/NVIDIA_A100_SXM4_80GB_EXPAND_TRUE.json b/vllm/lora/v1/ops/triton_ops/configs/NVIDIA_A100_SXM4_80GB_EXPAND_TRUE.json
new file mode 100644
index 0000000000000..0e48922e38329
--- /dev/null
+++ b/vllm/lora/v1/ops/triton_ops/configs/NVIDIA_A100_SXM4_80GB_EXPAND_TRUE.json
@@ -0,0 +1,4643 @@
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+}
\ No newline at end of file
diff --git a/vllm/lora/v1/ops/triton_ops/configs/NVIDIA_A100_SXM4_80GB_SHRINK.json b/vllm/lora/v1/ops/triton_ops/configs/NVIDIA_A100_SXM4_80GB_SHRINK.json
new file mode 100644
index 0000000000000..9a5091996f05c
--- /dev/null
+++ b/vllm/lora/v1/ops/triton_ops/configs/NVIDIA_A100_SXM4_80GB_SHRINK.json
@@ -0,0 +1,6038 @@
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+}
\ No newline at end of file
diff --git a/vllm/lora/v1/ops/triton_ops/configs/NVIDIA_H100_80GB_HBM3_EXPAND_FALSE.json b/vllm/lora/v1/ops/triton_ops/configs/NVIDIA_H100_80GB_HBM3_EXPAND_FALSE.json
new file mode 100644
index 0000000000000..66644fd794ae8
--- /dev/null
+++ b/vllm/lora/v1/ops/triton_ops/configs/NVIDIA_H100_80GB_HBM3_EXPAND_FALSE.json
@@ -0,0 +1,4643 @@
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+}
\ No newline at end of file
diff --git a/vllm/lora/v1/ops/triton_ops/configs/NVIDIA_H100_80GB_HBM3_EXPAND_TRUE.json b/vllm/lora/v1/ops/triton_ops/configs/NVIDIA_H100_80GB_HBM3_EXPAND_TRUE.json
new file mode 100644
index 0000000000000..453d0bca2ea66
--- /dev/null
+++ b/vllm/lora/v1/ops/triton_ops/configs/NVIDIA_H100_80GB_HBM3_EXPAND_TRUE.json
@@ -0,0 +1,4643 @@
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+}
\ No newline at end of file
diff --git a/vllm/lora/v1/ops/triton_ops/configs/NVIDIA_H100_80GB_HBM3_SHRINK.json b/vllm/lora/v1/ops/triton_ops/configs/NVIDIA_H100_80GB_HBM3_SHRINK.json
new file mode 100644
index 0000000000000..529991d331c8d
--- /dev/null
+++ b/vllm/lora/v1/ops/triton_ops/configs/NVIDIA_H100_80GB_HBM3_SHRINK.json
@@ -0,0 +1,6038 @@
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+          "max_nreg": null
+        }
+      },
+      "28672": {
+        "16": {
+          "block_m": 64,
+          "block_n": 16,
+          "block_k": 128,
+          "split_k": 8,
+          "num_warps": 4,
+          "num_ctas": 1,
+          "num_stages": 2,
+          "max_nreg": null
+        }
+      },
+      "32768": {
+        "16": {
+          "block_m": 64,
+          "block_n": 16,
+          "block_k": 128,
+          "split_k": 8,
+          "num_warps": 4,
+          "num_ctas": 1,
+          "num_stages": 2,
+          "max_nreg": null
+        }
+      }
+    }
+  }
+}
\ No newline at end of file
diff --git a/vllm/lora/v1/ops/triton_ops/utils.py b/vllm/lora/v1/ops/triton_ops/utils.py
new file mode 100644
index 0000000000000..5b2005362972d
--- /dev/null
+++ b/vllm/lora/v1/ops/triton_ops/utils.py
@@ -0,0 +1,98 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import functools
+import json
+import os
+from pathlib import Path
+from typing import Dict, Optional
+
+import torch
+
+
+@functools.lru_cache(maxsize=100)
+def load_v1_op_config(op_type: str,
+                      add_inputs: Optional[bool]) -> Optional[Dict]:
+    # TODO (varun) : op_type should be either 'v1_shrink' or 'v1_expand'
+    gpu_name = torch.cuda.get_device_name()
+    gpu_name = gpu_name.replace(' ', '_')
+    gpu_name = gpu_name.replace('-', '_')
+
+    config_fname = None
+    if op_type == "shrink":
+        config_fname = f"{gpu_name}_{op_type.upper()}.json"
+    else:
+        config_fname = (f"{gpu_name}_"
+                        f"{op_type.upper()}_"
+                        f"{str(add_inputs).upper()}.json")
+
+    config_path = Path(
+        f'{os.path.dirname(os.path.realpath(__file__))}/configs/{config_fname}'
+    )
+    if not config_path.exists():
+        return None
+
+    # TODO (varun) : It feels like there must be a utility to do this !!
+    # load json
+    config_data = None
+    with open(str(config_path)) as f:
+        config_data = json.load(f)
+    return config_data
+
+
+# TODO (varun) : Maybe rename !! Merge with the normal path !!
+@functools.lru_cache(maxsize=100)
+def get_v1_op_configs(op_type: str,
+                      batch: int,
+                      hidden_size: int,
+                      rank: int,
+                      num_slices: int,
+                      add_inputs: Optional[bool] = None) -> dict[str, int]:
+
+    assert op_type in ["shrink", "expand"]
+
+    # default config
+    default = {}
+    if op_type == "shrink":
+        default = {
+            'block_m': 32,
+            'block_n': 16,
+            'block_k': 256 if batch < 128 else 32,
+            'split_k': 64 if batch < 128 else 8,
+            'num_warps': 4,
+            'num_ctas': 1,
+            'num_stages': 2,
+            'max_nreg': None
+        }
+    else:
+        default = {
+            'block_m': 64,
+            'block_n': 128,
+            'block_k': 16,
+            'num_warps': 4,
+            'num_ctas': 1,
+            'num_stages': 2,
+            'max_nreg': None
+        }
+    m = batch
+
+    k, n = (hidden_size, rank) if op_type == "shrink" else (rank, hidden_size)
+
+    config_data = load_v1_op_config(op_type, add_inputs)
+    if not config_data:
+        return default
+
+    # config is structured as config_data[num_slices][m][k][n] = {}
+    # slice by num_slices
+    config_data = config_data[str(num_slices)]
+    # slice by m
+    config_data = config_data.get(str(m)) or config_data[min(
+        config_data.keys(), key=lambda x: abs(int(x) - m))]
+    # slice by k
+    config_data = config_data.get(str(k)) or config_data[min(
+        config_data.keys(), key=lambda x: abs(int(x) - k))]
+    # slice by n
+    config_data = config_data.get(str(n)) or config_data[min(
+        config_data.keys(), key=lambda x: abs(int(x) - n))]
+
+    assert config_data is not None
+    return config_data
diff --git a/vllm/lora/v1/ops/triton_ops/v1_expand.py b/vllm/lora/v1/ops/triton_ops/v1_expand.py
new file mode 100644
index 0000000000000..deb7119a543c0
--- /dev/null
+++ b/vllm/lora/v1/ops/triton_ops/v1_expand.py
@@ -0,0 +1,283 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+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 List
+
+import torch
+import triton
+import triton.language as tl
+
+from vllm.lora.ops.triton_ops.kernel_utils import do_expand_kernel
+from vllm.lora.ops.triton_ops.utils import _get_lora_b_ptr
+from vllm.utils import direct_register_custom_op
+
+from .utils import get_v1_op_configs
+
+
+@triton.jit
+def _v1_expand_kernel(
+        input_ptr,
+        lora_ptr,
+        out_ptr,
+        M,
+        N,
+        K,
+        token_indices_sorted_by_lora_ids,
+        num_tokens_per_lora,
+        lora_token_start_loc,
+        lora_ids,
+        slice_start_loc,
+        input_d0_stride,
+        input_d1_stride,
+        input_d2_stride,  # 1
+        ls_d0_ptr,
+        ls_d1_ptr,
+        ls_d2_ptr,  # 1
+        output_d0_stride,
+        output_d1_stride,  # 1
+        output_hs_ptr,
+        BLOCK_M: tl.constexpr,
+        BLOCK_N: tl.constexpr,
+        BLOCK_K: tl.constexpr,
+        EVEN_K: tl.constexpr,
+        ADD_INPUTS: tl.constexpr,
+        CAST_TYPE: tl.constexpr,
+        SLICE_NUM: tl.constexpr,
+        SAME_STRIDE: tl.constexpr):
+
+    cta_n_num = tl.cdiv(N, BLOCK_N)
+    cta_m_num = tl.cdiv(M, BLOCK_M)
+
+    pid_mn = tl.program_id(axis=0)
+    pid_m = pid_mn % cta_m_num
+    pid_n = (pid_mn // cta_m_num) % cta_n_num
+
+    slice_id = tl.program_id(axis=1)
+    lora_idx = tl.program_id(axis=2)
+
+    lora_id = tl.load(lora_ids + lora_idx)
+    if lora_id == -1:
+        # Early exit for the no-lora case.
+        return
+
+    lora_m_size = tl.load(num_tokens_per_lora + lora_idx)
+
+    cta_m_offset = pid_m * BLOCK_M
+    if cta_m_offset >= lora_m_size:
+        # Early exit CTA.
+        return
+
+    # When the output dimensions of each slice are the same,cur_n=N, otherwise
+    # cur_n=tl.load(output_hs_ptr + slice_id), this situation exists in GQA's
+    # qkv linear.
+    curr_N = N if SAME_STRIDE else tl.load(output_hs_ptr + slice_id)
+    if pid_n * BLOCK_N > curr_N:
+        # Early exit CTA.
+        return
+
+    # num rows this CTA should process.
+    cta_m_len = min(BLOCK_M, lora_m_size - cta_m_offset)
+
+    # Identify all rows that this CTA should process.
+    lora_m_indices_start = tl.load(lora_token_start_loc + lora_idx)
+    cta_lora_seq_indices = (token_indices_sorted_by_lora_ids +
+                            lora_m_indices_start + cta_m_offset)
+
+    # Load all relevant row indices.
+    offset_m = tl.arange(0, BLOCK_M) % cta_m_len
+    ram = tl.load(cta_lora_seq_indices + offset_m)
+
+    do_expand_kernel(
+        pid_n,
+        lora_id,
+        slice_id,
+        input_ptr,
+        lora_ptr,
+        out_ptr,
+        N,
+        K,
+        cta_m_len,
+        ram,  # array identifying the rows of Input ptr to operate on
+        slice_start_loc,
+        # input ptr strides
+        input_d0_stride,
+        input_d1_stride,
+        input_d2_stride,
+        # lora ptr strides
+        ls_d0_ptr,
+        ls_d1_ptr,
+        ls_d2_ptr,
+        # out ptr strides
+        output_d0_stride,
+        output_d1_stride,
+        # constants
+        BLOCK_M,
+        BLOCK_N,
+        BLOCK_K,
+        SAME_STRIDE,
+        SLICE_NUM,
+        EVEN_K,
+        CAST_TYPE,
+        ADD_INPUTS)
+
+
+@torch.inference_mode()
+def _v1_expand(
+    inputs: torch.Tensor,  # shape [num_slices, num_tokens, lora_rank]
+    lora_b_weights: List[
+        torch.Tensor],  # shape [num_lora, hidden_size, lora_rank]
+    output_tensor: torch.
+    Tensor,  # shape [num_tokens, hidden_size * num_slices]
+    token_lora_mapping: torch.Tensor,  # shape [num_tokens]
+    token_indices_sorted_by_lora_ids: torch.Tensor,  # shape [num_tokens]
+    num_tokens_per_lora: torch.Tensor,  # shape [max-loras + 1]
+    lora_token_start_loc: torch.Tensor,  # shape [max-loras + 2]
+    lora_ids: torch.Tensor,  # shape [max-loras + 1]
+    offset_start: int = 0,
+    add_inputs: bool = False,
+) -> None:
+    """
+    Args:
+        inputs (torch.Tensor): input tensor
+        lora_b_weights (List[torch.Tensor]): lora'b weight
+        output_tensor (torch.Tensor): output tensor
+        token_lora_mapping (torch.Tensor): A tensor mapping each input token
+            to the lora-id related to that token. A value of -1 indicates that
+            LoRA doesn't apply to that token.
+        token_indices_sorted_by_lora_ids (torch.Tensor): Row/Token indices from
+            the A matrix grouped by LoRA IDs.
+        num_tokens_per_lora (torch.Tensor): num_tokens_per_lora[i] is the number
+            of tokens that are to be processed by LoRA ID lora_ids[i] 
+        lora_token_start_loc (torch.Tensor): A cumulative sum of
+            num_tokens_per_lora. lora_token_start_loc[0] is always 0 so that
+            lora_token_start_loc[i], along with num_tokens_per_lora[i]
+            identifies the the region in token_indices_sorted_by_lora_ids that
+            LoRA lora_ids[i] should process.
+        lora_ids (torch.Tensor): LoRA ids to process.
+        offset_start (int, optional): Offset start for output_tensor. 
+            Defaults to 0.
+        add_inputs (bool, optional): Whether to add the input tensor to the 
+            output tensor. Defaults to False.
+    """
+    assert inputs.dtype in [torch.float16, torch.bfloat16, torch.float32]
+    for weight in lora_b_weights:
+        assert weight.dtype in [torch.float16, torch.bfloat16]
+
+    assert inputs.size(0) == len(lora_b_weights)
+    assert output_tensor.is_contiguous()
+
+    # metadata sanity check
+    assert token_lora_mapping.size(0) == token_indices_sorted_by_lora_ids.size(
+        0)
+    assert lora_ids.size(0) == num_tokens_per_lora.size(0)
+    assert lora_token_start_loc.size(0) == lora_ids.size(0) + 1
+
+    (slice_start_tensor, lora_ptr_tensor, lora_strides_d0_tensor,
+     lora_strides_d1_tensor, lora_strides_d2_tensor, hidden_sizes_tensor,
+     same_stride, MAX_N) = _get_lora_b_ptr(lora_b_weights, offset_start,
+                                           inputs.device)
+
+    K = lora_b_weights[0].shape[-1]  # K= rank
+    M = inputs.size(1)
+    ADD_INPUTS = add_inputs
+    MAX_LORAS = lora_ids.size(0)
+    CAST_TYPE = False
+    NUM_SLICES = len(lora_b_weights)
+
+    kernel_config = get_v1_op_configs(op_type="expand",
+                                      batch=M,
+                                      hidden_size=MAX_N,
+                                      rank=K,
+                                      num_slices=NUM_SLICES,
+                                      add_inputs=add_inputs)
+    BLOCK_M = kernel_config['block_m']
+    BLOCK_N = kernel_config['block_n']
+    BLOCK_K = kernel_config['block_k']
+    EVEN_K = K % BLOCK_K == 0
+
+    if inputs.dtype == torch.float32 and lora_b_weights[0].dtype in [
+            torch.float16,
+            torch.bfloat16,
+    ]:
+        CAST_TYPE = True
+
+    # TODO (varun): This grid formulation maximizes parallelization at the
+    # cost of wasteful thread block launch when only a few input tokens require
+    # LoRA. This might not be the best in all cases.
+    grid = (
+        triton.cdiv(M, BLOCK_M) * triton.cdiv(MAX_N, BLOCK_N),
+        NUM_SLICES,
+        # Each LoRA receives its own set of thread blocks for output
+        # computation. If some LoRA doesn't have any tokens to process, its
+        # thread blocks simply exit.
+        MAX_LORAS,
+    )
+
+    _v1_expand_kernel[grid](
+        inputs,
+        lora_ptr_tensor,
+        output_tensor,
+        M,
+        MAX_N,
+        K,
+        token_indices_sorted_by_lora_ids,
+        num_tokens_per_lora,
+        lora_token_start_loc,
+        lora_ids,
+        slice_start_tensor,
+        inputs.stride(0),
+        inputs.stride(1),
+        inputs.stride(2),
+        lora_strides_d0_tensor,
+        lora_strides_d1_tensor,
+        lora_strides_d2_tensor,
+        output_tensor.stride(0),
+        output_tensor.stride(1),
+        hidden_sizes_tensor,
+        BLOCK_M,
+        BLOCK_N,
+        BLOCK_K,
+        EVEN_K,
+        ADD_INPUTS,
+        CAST_TYPE,
+        NUM_SLICES,
+        same_stride,
+        num_warps=kernel_config['num_warps'],
+        num_ctas=kernel_config['num_ctas'],
+        num_stages=kernel_config['num_stages'],
+        maxnreg=kernel_config['max_nreg'],
+    )
+    return
+
+
+def _v1_expand_fake(
+    inputs: torch.Tensor,
+    lora_b_weights: List[torch.Tensor],
+    output_tensor: torch.Tensor,
+    token_lora_mapping: torch.Tensor,
+    token_indices_sorted_by_lora_ids: torch.Tensor,
+    num_tokens_per_lora: torch.Tensor,
+    lora_token_start_loc: torch.Tensor,
+    lora_ids: torch.Tensor,
+    offset_start: int = 0,
+    add_inputs: bool = False,
+) -> None:
+    return
+
+
+try:
+    direct_register_custom_op(
+        op_name="v1_expand",
+        op_func=_v1_expand,
+        mutates_args=["output_tensor"],
+        fake_impl=_v1_expand_fake,
+    )
+    v1_expand = torch.ops.vllm.v1_expand
+
+except AttributeError:
+    v1_expand = _v1_expand
diff --git a/vllm/lora/v1/ops/triton_ops/v1_shrink.py b/vllm/lora/v1/ops/triton_ops/v1_shrink.py
new file mode 100644
index 0000000000000..a69bbc5752367
--- /dev/null
+++ b/vllm/lora/v1/ops/triton_ops/v1_shrink.py
@@ -0,0 +1,238 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+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 List
+
+import torch
+import triton
+import triton.language as tl
+
+from vllm.lora.ops.triton_ops.kernel_utils import do_shrink_kernel
+from vllm.lora.ops.triton_ops.utils import _get_lora_a_ptr
+from vllm.utils import direct_register_custom_op
+
+from .utils import get_v1_op_configs
+
+
+@triton.jit
+def _v1_shrink_kernel(input_ptr, lora_ptr, out_ptr, M, N, K,
+                      token_indices_sorted_by_lora_ids, num_tokens_per_lora,
+                      lora_token_start_loc, lora_ids, scaling, input_d0_stride,
+                      input_d1_stride, lora_d0_stride, lora_d1_stride,
+                      lora_d2_stride, output_d0_stride, output_d1_stride,
+                      output_d2_stride, BLOCK_M: tl.constexpr,
+                      BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
+                      EVEN_K: tl.constexpr, SPLIT_K: tl.constexpr,
+                      SLICE_NUM: tl.constexpr):
+
+    cta_n_num = tl.cdiv(N, BLOCK_N)
+    cta_m_num = tl.cdiv(M, BLOCK_M)
+
+    pid_sk_m_n = tl.program_id(axis=0)
+    pid_sk = pid_sk_m_n % SPLIT_K
+    pid_m = (pid_sk_m_n // SPLIT_K) % cta_m_num
+    pid_n = pid_sk_m_n // (SPLIT_K * cta_m_num) % cta_n_num
+
+    slice_id = tl.program_id(axis=1)
+    lora_idx = tl.program_id(axis=2)
+
+    lora_id = tl.load(lora_ids + lora_idx)
+    if lora_id == -1:
+        # Early exit for the no-lora case.
+        return
+
+    lora_m_size = tl.load(num_tokens_per_lora + lora_idx)
+
+    cta_m_offset = pid_m * BLOCK_M
+    if cta_m_offset >= lora_m_size:
+        # Early exit CTA.
+        return
+
+    # num rows this CTA should process.
+    cta_m_len = min(BLOCK_M, lora_m_size - cta_m_offset)
+
+    # Identify all rows that this CTA should process.
+    lora_m_indices_start = tl.load(lora_token_start_loc + lora_idx)
+    cta_lora_seq_indices = (token_indices_sorted_by_lora_ids +
+                            lora_m_indices_start + cta_m_offset)
+
+    # Load all relevant row indices.
+    offset_m = tl.arange(0, BLOCK_M) % cta_m_len
+    ram = tl.load(cta_lora_seq_indices + offset_m)
+
+    do_shrink_kernel(
+        pid_n,
+        pid_sk,
+        slice_id,
+        lora_id,
+        input_ptr,
+        lora_ptr,
+        out_ptr,
+        N,
+        K,
+        cta_m_len,
+        ram,  # array identifying the rows of Input ptr to operate on
+        # input strides
+        input_d0_stride,
+        input_d1_stride,
+        # lora strides
+        lora_d0_stride,
+        lora_d1_stride,
+        lora_d2_stride,
+        # output strides
+        output_d0_stride,
+        output_d1_stride,
+        output_d2_stride,
+        scaling,
+        BLOCK_M,
+        BLOCK_N,
+        BLOCK_K,
+        EVEN_K,
+        SPLIT_K,
+        SLICE_NUM)
+
+
+@torch.inference_mode()
+def _v1_shrink(
+    inputs: torch.Tensor,  #  shape [num_tokens, hidden_size]
+    lora_a_weights: List[
+        torch.Tensor],  # shape [num_loras, lora_rank, hidden_size]
+    output_tensor: torch.Tensor,  # shape [num_slices, num_tokens, lora_rank]
+    token_lora_mapping: torch.Tensor,  # shape [num_tokens]
+    token_indices_sorted_by_lora_ids: torch.Tensor,  # shape [num_tokens] 
+    num_tokens_per_lora: torch.Tensor,  # shape [max-loras + 1]
+    lora_token_start_loc: torch.Tensor,  # shape [max-loras + 2]
+    lora_ids: torch.Tensor,  # shape [max-loras + 1]
+    scaling: float,
+) -> None:
+    """
+    Args:
+        inputs (torch.Tensor): Input tensor
+        lora_a_weights (List[torch.Tensor]): LoRA weights
+        output_tensor (torch.Tensor): output tensor
+        token_lora_mapping (torch.Tensor): A tensor mapping each input token
+            to the lora-id related to that token. A value of -1 indicates that
+            LoRA doesn't apply to that token.
+        token_indices_sorted_by_lora_ids (torch.Tensor): Row/Token indices from
+            the A matrix grouped by LoRA IDs.
+        num_tokens_per_lora (torch.Tensor): num_tokens_per_lora[i] is the number
+            of tokens that are to be processed by LoRA ID lora_ids[i] 
+        lora_token_start_loc (torch.Tensor): A cumulative sum of
+            num_tokens_per_lora. lora_token_start_loc[0] is always 0 so that
+            lora_token_start_loc[i], along with num_tokens_per_lora[i]
+            identifies the region in token_indices_sorted_by_lora_ids that
+            LoRA lora_ids[i] should process.
+        lora_ids (torch.Tensor): LoRA ids to process.
+        scaling (float): Scaling factor.
+    """
+    assert inputs.dtype == lora_a_weights[0].dtype
+    assert inputs.dtype in [torch.float16, torch.bfloat16]
+    for weight in lora_a_weights:
+        assert weight.dtype in [torch.float16, torch.bfloat16]
+
+    assert inputs.size(1) == lora_a_weights[0].size(-1)
+    assert inputs.is_contiguous()
+    assert output_tensor.is_contiguous()
+
+    # metadata sanity check
+    assert token_lora_mapping.size(0) == token_indices_sorted_by_lora_ids.size(
+        0)
+    assert lora_ids.size(0) == num_tokens_per_lora.size(0)
+    assert lora_token_start_loc.size(0) == lora_ids.size(0) + 1
+
+    (lora_ptr_tensor, lora_strides_d0, lora_strides_d1,
+     lora_strides_d2) = _get_lora_a_ptr(lora_a_weights, inputs.device)
+    N, K = lora_a_weights[0].shape[-2:]  # K=hidden_size,N=rank
+    M = inputs.size(0)
+    NUM_SLICES = len(lora_a_weights)
+
+    kernel_config = get_v1_op_configs("shrink",
+                                      batch=M,
+                                      hidden_size=K,
+                                      rank=N,
+                                      num_slices=NUM_SLICES)
+    BLOCK_M = kernel_config['block_m']
+    BLOCK_N = kernel_config['block_n']
+    BLOCK_K = kernel_config['block_k']
+    SPLIT_K = kernel_config['split_k']
+
+    EVEN_K = K % (BLOCK_K * SPLIT_K) == 0
+    MAX_LORAS = lora_ids.size(0)
+
+    # TODO (varun): This grid formulation maximizes parallelization at the
+    # cost of wasteful thread block launch when only few of the input tokens
+    # require LoRA. This might not be the best in all cases.
+    grid = (
+        SPLIT_K * triton.cdiv(M, BLOCK_M) * triton.cdiv(N, BLOCK_N),
+        NUM_SLICES,
+        # Each LoRA receives its own set of thread blocks for output
+        # computation. If some LoRA doesn't have any tokens to process, its
+        # thread blocks exit early.
+        MAX_LORAS,
+    )
+
+    _v1_shrink_kernel[grid](
+        inputs,
+        lora_ptr_tensor,
+        output_tensor,
+        M,
+        N,
+        K,
+        token_indices_sorted_by_lora_ids,
+        num_tokens_per_lora,
+        lora_token_start_loc,
+        lora_ids,
+        scaling,
+        inputs.stride(0),
+        inputs.stride(1),
+        lora_strides_d0,
+        lora_strides_d1,
+        lora_strides_d2,
+        output_tensor.stride(0),
+        output_tensor.stride(1),
+        output_tensor.stride(2),
+        BLOCK_M,
+        BLOCK_N,
+        BLOCK_K,
+        EVEN_K,
+        SPLIT_K,
+        NUM_SLICES,
+        num_warps=kernel_config['num_warps'],
+        num_ctas=kernel_config['num_ctas'],
+        num_stages=kernel_config['num_stages'],
+        maxnreg=kernel_config['max_nreg'],
+    )
+
+    return
+
+
+def _v1_shrink_fake(
+    inputs: torch.Tensor,
+    lora_a_weights: List[torch.Tensor],
+    output_tensor: torch.Tensor,
+    token_lora_mapping: torch.Tensor,
+    token_indices_sorted_by_lora_ids: torch.Tensor,
+    num_tokens_per_lora: torch.Tensor,
+    lora_token_start_loc: torch.Tensor,
+    lora_ids: torch.Tensor,
+    scaling: float,
+) -> None:
+    return
+
+
+try:
+    direct_register_custom_op(
+        op_name="v1_shrink",
+        op_func=_v1_shrink,
+        mutates_args=["output_tensor"],
+        fake_impl=_v1_shrink_fake,
+    )
+    v1_shrink = torch.ops.vllm.v1_shrink
+
+except AttributeError:
+    v1_shrink = _v1_shrink
diff --git a/vllm/lora/v1/punica_wrapper/punica_gpu_v1.py b/vllm/lora/v1/punica_wrapper/punica_gpu_v1.py
new file mode 100644
index 0000000000000..8a83d69dd7095
--- /dev/null
+++ b/vllm/lora/v1/punica_wrapper/punica_gpu_v1.py
@@ -0,0 +1,371 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+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 dataclasses import dataclass
+from typing import TYPE_CHECKING, List, Optional, Tuple, Union, final
+
+import torch
+
+from vllm.lora.layers import LoRAMapping
+from vllm.triton_utils import HAS_TRITON
+
+if HAS_TRITON:
+    from vllm.lora.v1.ops.triton_ops import v1_expand
+    from vllm.lora.v1.ops.triton_ops import v1_shrink
+
+from vllm.lora.punica_wrapper.punica_base import PunicaWrapperBase
+
+if TYPE_CHECKING:
+    # avoid circuit import
+    from vllm.lora.models import LongContextLoRAContext
+
+
+@dataclass
+class V1KernelMeta:
+    token_lora_mapping: torch.Tensor
+    token_indices_sorted_by_lora_ids: torch.Tensor
+    active_lora_ids: torch.Tensor
+    num_tokens_per_lora: torch.Tensor
+    lora_token_start_loc: torch.Tensor
+
+    @staticmethod
+    def make(max_loras: int, max_num_tokens: int,
+             device: torch.device) -> "V1KernelMeta":
+
+        token_lora_mapping = torch.empty(max_num_tokens,
+                                         dtype=torch.int32,
+                                         device=device)
+
+        token_indices_sorted_by_lora_ids = torch.empty(max_num_tokens,
+                                                       dtype=torch.int32,
+                                                       device=device)
+
+        # +1 because "no-lora" is also a possibility
+        # example: let max_loras be 3, active_lora_ids of [-1, 0, 2, 1]
+        # is a possibility.
+        active_lora_ids = torch.empty(max_loras + 1,
+                                      dtype=torch.int32,
+                                      device=device)
+
+        # using running example, [3, 10, 5, 2] is a possibility.
+        num_tokens_per_lora = torch.zeros(max_loras + 1,
+                                          dtype=torch.int32,
+                                          device=device)
+
+        # +2 for this because, the first index is always 0
+        # for example: let max loras be 3, then lora_token_start_loc,
+        # can be [0, 3, 13, 18, 20].
+        lora_token_start_loc = torch.zeros(max_loras + 2,
+                                           dtype=torch.int32,
+                                           device=device)
+        return V1KernelMeta(
+            token_lora_mapping=token_lora_mapping,
+            token_indices_sorted_by_lora_ids=token_indices_sorted_by_lora_ids,
+            active_lora_ids=active_lora_ids,
+            num_tokens_per_lora=num_tokens_per_lora,
+            lora_token_start_loc=lora_token_start_loc)
+
+    def reset(self):
+        self.active_lora_ids.fill_(-1)
+        self.num_tokens_per_lora.fill_(0)
+        self.lora_token_start_loc.fill_(0)
+
+    def prepare_tensors(self, token_lora_mapping: torch.Tensor) -> None:
+
+        self.reset()
+
+        num_tokens = token_lora_mapping.size(0)
+
+        # copy token lora mapping
+        self.token_lora_mapping[:num_tokens].copy_(token_lora_mapping,
+                                                   non_blocking=True)
+
+        # token_indices_sorted_by_lora_ids
+        _, token_indices_sorted_by_lora_ids = torch.sort(token_lora_mapping,
+                                                         stable=True)
+        # start gpu transfer
+        self.token_indices_sorted_by_lora_ids[:num_tokens].copy_(
+            token_indices_sorted_by_lora_ids, non_blocking=True)
+
+        # active_lora_ids, num_tokens_per_lora
+        lora_ids, num_tokens_per_lora = torch.unique(token_lora_mapping,
+                                                     sorted=False,
+                                                     return_counts=True)
+        self.active_lora_ids[:lora_ids.size(0)].copy_(lora_ids,
+                                                      non_blocking=True)
+        self.num_tokens_per_lora[:num_tokens_per_lora.size(0)].copy_(
+            num_tokens_per_lora, non_blocking=True)
+
+        # lora_token_start_loc
+        lora_token_start_loc = torch.cumsum(num_tokens_per_lora, dim=0)
+        self.lora_token_start_loc[1:1 + lora_token_start_loc.size(0)].copy_(
+            lora_token_start_loc, non_blocking=True)
+
+    def meta_args(
+        self, num_tokens: int
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor,
+               torch.Tensor]:
+        return (self.token_lora_mapping[:num_tokens],
+                self.token_indices_sorted_by_lora_ids[:num_tokens],
+                self.num_tokens_per_lora, self.lora_token_start_loc,
+                self.active_lora_ids)
+
+
+@final
+class PunicaWrapperGPUV1(PunicaWrapperBase):
+
+    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)
+        self.max_loras = kwargs['max_loras']
+        self.token_mapping_v1_meta = V1KernelMeta.make(self.max_loras,
+                                                       max_num_batched_tokens,
+                                                       device=device)
+        self.prompt_mapping_v1_meta = V1KernelMeta.make(self.max_loras,
+                                                        max_batches,
+                                                        device=device)
+
+    def update_metadata(
+            self,
+            mapping: LoRAMapping,
+            lora_index_to_id: List[Optional[int]],
+            max_loras: int,
+            vocab_size: int,
+            extra_vocab_size: int,
+            long_lora_context: Optional["LongContextLoRAContext"] = None,
+            **kwargs):
+        self.update_base_metadata(mapping, lora_index_to_id, max_loras,
+                                  vocab_size, extra_vocab_size,
+                                  long_lora_context)
+        self.token_mapping_v1_meta.prepare_tensors(self.token_lora_indices)
+        self.prompt_mapping_v1_meta.prepare_tensors(self.sampler_indices)
+
+    def _apply_shrink(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        w_t_all: Tuple[torch.Tensor, ...],
+        scale: float,
+    ):
+        # TODO (varun): Handle no_lora case - we can skip the op entirely.
+        v1_shrink(
+            x,
+            w_t_all,
+            y,
+            *self.token_mapping_v1_meta.meta_args(x.size(0)),
+            scale,
+        )
+
+    def _apply_expand(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        w_t_all: torch.Tensor,
+        offset_start: int,
+        add_inputs: bool,
+    ):
+        # TODO (varun): Handle no_lora case - we can skip the op entirely.
+        v1_expand(
+            x,
+            w_t_all,
+            y,
+            *self.token_mapping_v1_meta.meta_args(x.size(0)),
+            offset_start=offset_start,
+            add_inputs=add_inputs,
+        )
+
+    def add_shrink(self, y: Union[Tuple[torch.Tensor, ...], torch.Tensor],
+                   x: torch.Tensor, lora_a_stacked: Tuple[torch.Tensor, ...],
+                   scale: float, **kwargs):
+        """
+        Performs GEMM  for multiple slices of lora_a.
+        When `is_prefill is` true, it indicates that it is currently the
+        prefill stage, and the `_shrink_prefill` function should be called.
+        Otherwise, it is the decode stage, and the _shrink_decode function
+        should be called.
+            
+        Semantics:
+        for i in range(len(lora_a_stacked)):
+            y[i] += (x @ lora_a_stacked[i]) * scale
+        
+        Args:
+            y (Union[Tuple[torch.Tensor, ...], 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])
+        self._apply_shrink(y, x, lora_a_stacked, scale)
+
+    def add_expand(self,
+                   y: torch.Tensor,
+                   x: Union[Tuple[torch.Tensor, ...], 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 (Union[Tuple[torch.Tensor, ...], 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:
+            self._apply_bias(self.token_lora_indices, y, output_slices,
+                             lora_bias_stacked)
+
+        # NOTE fused kernel
+        self._apply_expand(y, x, lora_b_stacked, offset_start, add_inputs=True)
+        y = 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.
+        """
+        v1_expand(
+            x.unsqueeze(dim=0),
+            [lora_b_stacked],
+            y,
+            *self.token_mapping_v1_meta.meta_args(x.size(1)),
+            offset_start=0,
+            add_inputs=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[Tuple[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[Tuple[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)
+            y = self._apply_bias(self.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(
+                (len(output_slices), x.size(0), r),
+                dtype=torch.float32,
+                device=x.device,
+            )
+        self.add_shrink(buffer, x, lora_a_stacked, scale, **kwargs)
+        self.add_expand(y,
+                        buffer,
+                        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)
+        num_slices = lora_a_stacked.size(1)
+        assert num_slices == 1, "lora for logits always has only 1 slice"
+
+        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((num_slices, x.size(0), r),
+                                 dtype=torch.float32,
+                                 device=x.device)
+
+        v1_shrink(x, [lora_a_stacked], buffer,
+                  *self.prompt_mapping_v1_meta.meta_args(x.size(0)), scale)
+
+        v1_expand(buffer, [lora_b_stacked],
+                  y,
+                  *self.prompt_mapping_v1_meta.meta_args(buffer.size(0)),
+                  add_inputs=True)
+        y = y.view_as(y_org)
diff --git a/vllm/platforms/cuda.py b/vllm/platforms/cuda.py
index 9deb0294668ec..6b34ad07d6aba 100644
--- a/vllm/platforms/cuda.py
+++ b/vllm/platforms/cuda.py
@@ -232,7 +232,10 @@ def get_attn_backend_cls(cls, selected_backend, head_size, dtype,
 
     @classmethod
     def get_punica_wrapper(cls) -> str:
-        return "vllm.lora.punica_wrapper.punica_gpu.PunicaWrapperGPU"
+        if envs.VLLM_USE_V1:
+            return "vllm.lora.v1.punica_wrapper.punica_gpu_v1.PunicaWrapperGPUV1"  # noqa
+        else:
+            return "vllm.lora.punica_wrapper.punica_gpu.PunicaWrapperGPU"
 
 
 # NVML utils
diff --git a/vllm/v1/worker/lora_model_runner_mixin.py b/vllm/v1/worker/lora_model_runner_mixin.py
index e7501ad2ea168..7e63d40777dbf 100644
--- a/vllm/v1/worker/lora_model_runner_mixin.py
+++ b/vllm/v1/worker/lora_model_runner_mixin.py
@@ -63,9 +63,9 @@ def _set_active_loras(self, prompt_lora_mapping: Tuple[int, ...],
         if not self.lora_manager:
             raise RuntimeError("LoRA is not enabled.")
 
-        # We dont make any distinction between prefills and decodes in the
-        # scheduler. To that effect, set is_prefill to True so we use the
-        # sgmv punica kernels always.
+        # Set is_prefill to True, so we always use the SGMV kernels.
+        # For cuda platforms, we have specialized triton kernels, and
+        # the cuda path ignores `is_prefill`.
         lora_mapping = LoRAMapping(token_lora_mapping,
                                    prompt_lora_mapping,
                                    is_prefill=True)