diff --git a/benchmarks/kernels/benchmark_mrope.py b/benchmarks/kernels/benchmark_mrope.py
new file mode 100644
index 0000000000000..b9147361708fd
--- /dev/null
+++ b/benchmarks/kernels/benchmark_mrope.py
@@ -0,0 +1,328 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# This script benchmarks the mrope kernel (mainly for Qwen2VL and Qwen2.5VL models).
+# It generates test data, runs benchmarks, and saves results to a CSV file.
+#
+# The CSV file (named with current date/time) contains these columns:
+# model_name, tp_size, num_tokens, num_heads, num_kv_heads, head_dim, max_position,
+# rope_theta, is_neox_style, rope_scaling, dtype, torch_mean, torch_median, torch_p99,
+# torch_min, torch_max, triton_mean, triton_median, triton_p99, triton_min, triton_max,
+# speedup
+#
+# == Usage Examples ==
+#
+# Single model benchmark:
+# python3 benchmark_mrope.py --model-name Qwen/Qwen2-VL-7B-Instruct --tp-size 1 \
+#   --warmup-iter 10 --benchmark-iter 100 --dtype bfloat16 --seed 0 --num-tokens 1024
+#
+# All models benchmark:
+# python3 benchmark_mrope.py --model-name "" --tp-size 1 --warmup-iter 10 \
+#   --benchmark-iter 100 --dtype bfloat16 --seed 0 --num-tokens 1024
+#
+# All models with different TP sizes:
+# python3 benchmark_mrope.py --model-name "" --tp-size 1 2 4 8 --warmup-iter 10 \
+#   --benchmark-iter 100 --dtype bfloat16 --seed 0 --num-tokens 1024
+#
+# All models with different token counts:
+# python3 benchmark_mrope.py --model-name "" --tp-size 1 --warmup-iter 10 \
+#   --benchmark-iter 100 --dtype bfloat16 --seed 0 --num-tokens 1024 4096 16384
+import csv
+import os
+import time
+from datetime import datetime
+from typing import Any
+
+import numpy as np
+import torch
+
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.platforms import current_platform
+from vllm.transformers_utils.config import get_config
+from vllm.utils import FlexibleArgumentParser
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def generate_test_data(
+    num_tokens: int,
+    num_q_heads: int,
+    num_kv_heads: int,
+    head_size: int,
+    max_position_embeddings: int,
+    dtype: torch.dtype,
+    device: torch.device,
+):
+    """Generate test data for given configuration."""
+    # Create 2D positions (3, num_tokens) for multimodal case
+    positions = torch.randint(
+        0, max_position_embeddings // 4, (3, num_tokens), device=device
+    )
+
+    # Create query and key tensors
+    query = torch.randn(num_tokens, num_q_heads * head_size, dtype=dtype, device=device)
+    key = torch.randn(num_tokens, num_kv_heads * head_size, dtype=dtype, device=device)
+
+    return positions, query, key
+
+
+def calculate_stats(times: list[float]) -> dict[str, float]:
+    """Calculate statistics from a list of times."""
+    times_array = np.array(times)
+    return {
+        "mean": np.mean(times_array),
+        "median": np.median(times_array),
+        "p99": np.percentile(times_array, 99),
+        "min": np.min(times_array),
+        "max": np.max(times_array),
+    }
+
+
+def benchmark_mrope(
+    model_name: str,
+    num_tokens: int,
+    head_dim: int,
+    tp_size: int,
+    num_heads: int,
+    num_kv_heads: int,
+    max_position: int = 8192,
+    rope_theta: float = 10000,
+    is_neox_style: bool = True,
+    rope_scaling: dict[str, Any] = None,
+    dtype: torch.dtype = torch.bfloat16,
+    seed: int = 0,
+    warmup_iter: int = 10,
+    benchmark_iter: int = 100,
+    csv_writer=None,
+):
+    current_platform.seed_everything(seed)
+    torch.set_default_device(device)
+    # the parameters to compute the q k v size based on tp_size
+    mrope_helper_class = get_rope(
+        head_size=head_dim,
+        rotary_dim=head_dim,
+        max_position=max_position,
+        base=rope_theta,
+        is_neox_style=is_neox_style,
+        rope_scaling=rope_scaling,
+        dtype=dtype,
+    ).to(device=device)
+
+    print(80 * "=")
+    print(
+        f"Evaluating model: {model_name} "
+        f"with tp_size: {tp_size} "
+        f"and num_tokens: {num_tokens}, "
+        f"dtype: {dtype}"
+    )
+
+    # create q k v input tensors
+    # create rotary pos emb input tensors
+    positions, query, key = generate_test_data(
+        num_tokens, num_heads, num_kv_heads, head_dim, max_position, dtype, device
+    )
+
+    # Warm up
+    for _ in range(warmup_iter):
+        mrope_helper_class.forward_native(
+            positions,
+            query.clone(),
+            key.clone(),
+        )
+
+        mrope_helper_class.forward_cuda(
+            positions,
+            query.clone(),
+            key.clone(),
+        )
+
+    torch.cuda.synchronize()
+
+    # Time reference implementation
+    torch_times = []
+    for _ in range(benchmark_iter):
+        query_clone = query.clone()
+        key_clone = key.clone()
+        torch.cuda.synchronize()
+        start_time = time.time()
+
+        mrope_helper_class.forward_native(
+            positions,
+            query_clone,
+            key_clone,
+        )
+
+        torch.cuda.synchronize()
+        torch_times.append(time.time() - start_time)
+
+    # Time triton kernel implementation
+    triton_times = []
+    for _ in range(benchmark_iter):
+        query_clone = query.clone()
+        key_clone = key.clone()
+        torch.cuda.synchronize()
+        start_time = time.time()
+        mrope_helper_class.forward_cuda(
+            positions,
+            query_clone,
+            key_clone,
+        )
+        torch.cuda.synchronize()
+        triton_times.append(time.time() - start_time)
+
+    # Calculate statistics
+    torch_stats = calculate_stats(torch_times)
+    triton_stats = calculate_stats(triton_times)
+    print(f"\nPerformance for config ({num_tokens}, {num_heads}, {num_kv_heads}):")
+
+    print(
+        f"Torch implementation: "
+        f"mean={torch_stats['mean']:.8f}s, "
+        f"median={torch_stats['median']:.8f}s, "
+        f"p99={torch_stats['p99']:.8f}s"
+    )
+
+    print(
+        f"Triton implementation: "
+        f"mean={triton_stats['mean']:.8f}s, "
+        f"median={triton_stats['median']:.8f}s, "
+        f"p99={triton_stats['p99']:.8f}s"
+    )
+
+    print(
+        f"Triton Speedup over Torch: {torch_stats['mean'] / triton_stats['mean']:.8f}x"
+    )
+
+    # Write to CSV
+    if csv_writer:
+        row = [
+            model_name,
+            tp_size,
+            num_tokens,
+            num_heads,
+            num_kv_heads,
+            head_dim,
+            max_position,
+            rope_theta,
+            is_neox_style,
+            str(rope_scaling),
+            str(dtype).split(".")[-1],
+            torch_stats["mean"],
+            torch_stats["median"],
+            torch_stats["p99"],
+            torch_stats["min"],
+            torch_stats["max"],
+            triton_stats["mean"],
+            triton_stats["median"],
+            triton_stats["p99"],
+            triton_stats["min"],
+            triton_stats["max"],
+            torch_stats["mean"] / triton_stats["mean"],  # speedup
+        ]
+        csv_writer.writerow(row)
+
+    return torch_stats, triton_stats
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser(
+        description="Benchmark the rotary embedding kernels."
+    )
+    parser.add_argument("--model-name", type=str, default="")
+    parser.add_argument("--tp-size", type=int, default=1)
+    parser.add_argument("--warmup-iter", type=int, default=10)
+    parser.add_argument("--benchmark-iter", type=int, default=100)
+    parser.add_argument("--dtype", type=str, choices=["bfloat16"], default="bfloat16")
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--num-tokens", type=int, nargs="+", required=False)
+    parser.add_argument("--trust-remote-code", action="store_true")
+    parser.add_argument("--output-csv", type=str, default="mrope_benchmark_results.csv")
+    args = parser.parse_args()
+    print(args)
+
+    # Create CSV file for results
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    csv_filename = f"{os.path.splitext(args.output_csv)[0]}_{timestamp}.csv"
+
+    with open(csv_filename, "w", newline="") as csvfile:
+        csv_writer = csv.writer(csvfile)
+        # Write header
+        header = [
+            "model_name",
+            "tp_size",
+            "num_tokens",
+            "num_heads",
+            "num_kv_heads",
+            "head_dim",
+            "max_position",
+            "rope_theta",
+            "is_neox_style",
+            "rope_scaling",
+            "dtype",
+            "torch_mean",
+            "torch_median",
+            "torch_p99",
+            "torch_min",
+            "torch_max",
+            "triton_mean",
+            "triton_median",
+            "triton_p99",
+            "triton_min",
+            "triton_max",
+            "speedup",
+        ]
+        csv_writer.writerow(header)
+
+        model_tp_dict = {}
+        if args.model_name == "":
+            model_tp_dict = {
+                "Qwen/Qwen2-VL-2B-Instruct": [1],
+                "Qwen/Qwen2-VL-7B-Instruct": [1],
+                "Qwen/Qwen2-VL-72B-Instruct": [2, 4, 8],
+                "Qwen/Qwen2.5-VL-3B-Instruct": [1, 2, 4, 8],
+                "Qwen/Qwen2.5-VL-7B-Instruct": [1, 2, 4, 8],
+                "Qwen/Qwen2.5-VL-72B-Instruct": [2, 4, 8],
+            }
+        else:
+            model_tp_dict[args.model_name] = [args.tp_size]
+
+        if args.num_tokens is None:
+            num_tokens_list = [2**i for i in range(0, 18)]
+        else:
+            num_tokens_list = args.num_tokens
+
+        for model_name, tp_list in model_tp_dict.items():
+            config = get_config(model_name, trust_remote_code=args.trust_remote_code)
+            for tp_size in tp_list:
+                # get the model config
+                total_num_kv_heads = config.num_key_value_heads
+                total_num_heads = config.num_attention_heads
+                num_heads = total_num_heads // tp_size
+                num_kv_heads = max(1, total_num_kv_heads // tp_size)
+                head_dim = config.hidden_size // total_num_heads
+                q_size = num_heads * head_dim
+                kv_size = num_kv_heads * head_dim
+                is_neox_style = True
+                rope_theta = config.rope_theta
+                max_position = config.max_position_embeddings
+
+                for num_tokens in num_tokens_list:
+                    benchmark_mrope(
+                        model_name=model_name,
+                        num_tokens=num_tokens,
+                        head_dim=head_dim,
+                        tp_size=tp_size,
+                        num_heads=num_heads,
+                        num_kv_heads=num_kv_heads,
+                        max_position=max_position,
+                        rope_theta=rope_theta,
+                        is_neox_style=is_neox_style,
+                        rope_scaling=config.rope_scaling,
+                        dtype=getattr(torch, args.dtype),
+                        seed=args.seed,
+                        warmup_iter=args.warmup_iter,
+                        benchmark_iter=args.benchmark_iter,
+                        csv_writer=csv_writer,
+                    )
+
+    print(f"Benchmark results saved to {csv_filename}")
diff --git a/tests/kernels/test_mrope.py b/tests/kernels/test_mrope.py
new file mode 100644
index 0000000000000..5918b7a58b5c0
--- /dev/null
+++ b/tests/kernels/test_mrope.py
@@ -0,0 +1,207 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch
+from transformers import AutoConfig
+
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.platforms import current_platform
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def generate_test_data(num_tokens: int, num_q_heads: int, num_kv_heads: int,
+                       head_size: int, max_position_embeddings: int,
+                       dtype: torch.dtype, device: torch.device):
+    """Generate test data for given configuration."""
+    # Create 2D positions (3, num_tokens) for multimodal case
+    positions = torch.randint(0,
+                              max_position_embeddings // 4, (3, num_tokens),
+                              device=device)
+
+    # Create query and key tensors
+    query = torch.randn(num_tokens,
+                        num_q_heads * head_size,
+                        dtype=dtype,
+                        device=device)
+    key = torch.randn(num_tokens,
+                      num_kv_heads * head_size,
+                      dtype=dtype,
+                      device=device)
+
+    return positions, query, key
+
+
+def unroll_model_tp_dict(model_tp_dict):
+    return [(model_name, tp_size)
+            for model_name, tp_sizes in model_tp_dict.items()
+            for tp_size in tp_sizes]
+
+
+model_tp_dict = {
+    "Qwen/Qwen2-VL-7B-Instruct": [1, 2],
+    "Qwen/Qwen2-VL-72B-Instruct": [1, 2],
+    "Qwen/Qwen2.5-VL-72B-Instruct": [1, 2]
+}
+
+# https://github.com/pytorch/pytorch/blob/main/torch/testing/_comparison.py#L1317
+dtype_atol_rtol_list = [
+    [torch.bfloat16, 1e-5, 1.6e-2],
+]
+
+num_tokens_list = [11, 8192]
+
+
+@pytest.mark.skipif(not current_platform.is_cuda_alike(),
+                    reason="Skipping CUDA/ROCm only tests.")
+@pytest.mark.parametrize("model_name, tp_size",
+                         unroll_model_tp_dict(model_tp_dict))
+@pytest.mark.parametrize("dtype, atol, rtol", dtype_atol_rtol_list)
+@pytest.mark.parametrize("num_tokens", num_tokens_list)
+def test_mrope(model_name, tp_size, dtype, atol, rtol, num_tokens):
+
+    config = AutoConfig.from_pretrained(model_name)
+
+    # get the model config
+    total_num_kv_heads = config.num_key_value_heads
+    total_num_heads = config.num_attention_heads
+    num_heads = total_num_heads // tp_size
+    num_kv_heads = max(1, total_num_kv_heads // tp_size)
+    head_dim = config.hidden_size // total_num_heads
+    is_neox_style = True
+
+    rope_theta = config.rope_theta
+    max_position = config.max_position_embeddings
+
+    mrope_helper_class = get_rope(
+        head_size=head_dim,
+        rotary_dim=head_dim,
+        max_position=max_position,
+        base=rope_theta,
+        is_neox_style=is_neox_style,
+        rope_scaling=config.rope_scaling,
+        dtype=dtype,
+    ).to(device=device)
+
+    # create q k v input tensors
+    # create rotary pos emb input tensors
+    positions, query, key = generate_test_data(num_tokens, num_heads,
+                                               num_kv_heads, head_dim,
+                                               max_position, dtype, device)
+
+    query_native, key_native = mrope_helper_class.forward_native(
+        positions,
+        query.clone(),
+        key.clone(),
+    )
+
+    query_cuda, key_cuda = mrope_helper_class.forward_cuda(
+        positions,
+        query.clone(),
+        key.clone(),
+    )
+
+    torch.testing.assert_close(query_native, query_cuda, atol=atol, rtol=rtol)
+    torch.testing.assert_close(key_native, key_cuda, atol=atol, rtol=rtol)
+
+
+@pytest.mark.skipif(not current_platform.is_cuda_alike(),
+                    reason="Skipping CUDA/ROCm only tests.")
+@pytest.mark.parametrize(
+    "model_name, tp_size",
+    unroll_model_tp_dict({"Qwen/Qwen2-VL-7B-Instruct": [1, 2]}))
+@pytest.mark.parametrize("dtype, atol, rtol", dtype_atol_rtol_list)
+@pytest.mark.parametrize("num_tokens", [4])
+def test_mrope_torch_compile_tracing(model_name, tp_size, dtype, atol, rtol,
+                                     num_tokens):
+    config = AutoConfig.from_pretrained(model_name)
+
+    # get the model config
+    total_num_kv_heads = config.num_key_value_heads
+    total_num_heads = config.num_attention_heads
+    num_heads = total_num_heads // tp_size
+    num_kv_heads = max(1, total_num_kv_heads // tp_size)
+    head_dim = config.hidden_size // total_num_heads
+    is_neox_style = True
+    rope_theta = config.rope_theta
+    max_position = config.max_position_embeddings
+
+    mrope_helper_class = get_rope(
+        head_size=head_dim,
+        rotary_dim=head_dim,
+        max_position=max_position,
+        base=rope_theta,
+        is_neox_style=is_neox_style,
+        rope_scaling=config.rope_scaling,
+        dtype=dtype,
+    ).to(device=device)
+
+    # Generate test data
+    positions, query, key = generate_test_data(num_tokens, num_heads,
+                                               num_kv_heads, head_dim,
+                                               max_position, dtype, device)
+
+    # Create a wrapper that makes the in-place function appear functional
+    def functional_forward_cuda(pos, q, k):
+        """Wrapper that converts in-place operation to functional style
+        
+        CUDA Graph does not support in-place operations.
+        This wrapper creates working copies of the 
+        input tensors and modifies them.
+        """
+        q_work = q.clone()  # Create working copies
+        k_work = k.clone()
+        # Your in-place function modifies q_work and k_work
+        mrope_helper_class.forward_cuda(pos, q_work, k_work)
+        return q_work, k_work  # Return the modified tensors
+
+    # Get reference results
+    query_native, key_native = mrope_helper_class.forward_native(
+        positions,
+        query.clone(),
+        key.clone(),
+    )
+
+    try:
+        compiled_forward_cuda = torch.compile(functional_forward_cuda,
+                                              fullgraph=True,
+                                              backend="inductor",
+                                              mode="reduce-overhead",
+                                              dynamic=False)
+
+        # Run compiled version
+        query_compiled_cuda, key_compiled_cuda = compiled_forward_cuda(
+            positions,
+            query,
+            key,
+        )
+
+        # Run original version for comparison
+        query_cuda = query.clone()
+        key_cuda = key.clone()
+        mrope_helper_class.forward_cuda(positions, query_cuda, key_cuda)
+
+        # Verify results
+        torch.testing.assert_close(query_compiled_cuda,
+                                   query_cuda,
+                                   atol=atol,
+                                   rtol=rtol)
+        torch.testing.assert_close(key_compiled_cuda,
+                                   key_cuda,
+                                   atol=atol,
+                                   rtol=rtol)
+        torch.testing.assert_close(query_compiled_cuda,
+                                   query_native,
+                                   atol=atol,
+                                   rtol=rtol)
+        torch.testing.assert_close(key_compiled_cuda,
+                                   key_native,
+                                   atol=atol,
+                                   rtol=rtol)
+
+        print("✓ forward_cuda successfully traced with torch.compile inductor")
+
+    except Exception as e:
+        pytest.fail(
+            f"forward_cuda failed to trace with torch.compile inductor: {e}")
diff --git a/vllm/model_executor/layers/rotary_embedding/mrope.py b/vllm/model_executor/layers/rotary_embedding/mrope.py
index a75b9e5eb435c..d3b71930b6f17 100644
--- a/vllm/model_executor/layers/rotary_embedding/mrope.py
+++ b/vllm/model_executor/layers/rotary_embedding/mrope.py
@@ -8,10 +8,173 @@ import numpy as np
 import torch
 from transformers import PretrainedConfig
 
+from vllm.platforms import current_platform
+from vllm.triton_utils import tl, triton
+
 from .base import RotaryEmbedding
 from .common import apply_rotary_emb_dispatch
 
 
+@triton.jit
+def _triton_qwen2vl_mrope_forward(
+    q_ptr,
+    k_ptr,
+    cos,
+    sin,
+    num_tokens,
+    n_qh: tl.constexpr,
+    n_kh: tl.constexpr,
+    hd: tl.constexpr,
+    pad_n_qh: tl.constexpr,
+    pad_n_kh: tl.constexpr,
+    pad_hd: tl.constexpr,
+    mrope_section_t: tl.constexpr,
+    mrope_section_h: tl.constexpr,
+):
+    # Adapted from
+    # https://github.com/linkedin/Liger-Kernel/blob/main/src/liger_kernel/ops/qwen2vl_mrope.py
+    # This version supports flatten input tensors from vllm
+    # and supports cos and sin cache with shape (3, num_tokens, head_dim // 2)
+    # instead of (3, bsz, seq_len, head_dim)
+    pid = tl.program_id(0)
+    # locate start address
+    q_ptr = q_ptr + pid * (n_qh * hd)
+    k_ptr = k_ptr + pid * (n_kh * hd)
+
+    # ####################################################################
+    # get the cos(mθ_{i...d/2}) and sin(mθ_{i...d/2}) for token position
+    # m of this program instance
+    # ####################################################################
+    # Note: cos and sin now have shape (3, num_tokens, head_dim // 2)
+
+    t_end = mrope_section_t
+    h_end = t_end + mrope_section_h
+
+    # Updated stride calculation for half head_dim
+    half_hd = hd // 2
+    t_cos = cos + pid * half_hd
+    h_cos = t_cos + num_tokens * half_hd
+    w_cos = h_cos + num_tokens * half_hd
+    t_sin = sin + pid * half_hd
+    h_sin = t_sin + num_tokens * half_hd
+    w_sin = h_sin + num_tokens * half_hd
+
+    # Updated offsets for half head_dim
+    cos_offsets = tl.arange(0, pad_hd // 2)
+    t_mask = cos_offsets < t_end
+    h_mask = (t_end <= cos_offsets) & (cos_offsets < h_end)
+    w_mask = (h_end <= cos_offsets) & (cos_offsets < half_hd)
+
+    t_cos_row = tl.load(t_cos + cos_offsets, mask=t_mask, other=0)
+    h_cos_row = tl.load(h_cos + cos_offsets, mask=h_mask, other=0)
+    w_cos_row = tl.load(w_cos + cos_offsets, mask=w_mask, other=0)
+    t_sin_row = tl.load(t_sin + cos_offsets, mask=t_mask, other=0)
+    h_sin_row = tl.load(h_sin + cos_offsets, mask=h_mask, other=0)
+    w_sin_row = tl.load(w_sin + cos_offsets, mask=w_mask, other=0)
+
+    cos_row = t_cos_row + h_cos_row + w_cos_row
+    sin_row = t_sin_row + h_sin_row + w_sin_row
+
+    # ####################################################################
+    # Load the left and right half of q and k for the current
+    # program instance (i.e. for the current token) separately
+    # ####################################################################
+    # left half of the head
+    first_half_q_offsets = tl.arange(0, pad_n_qh)[:, None] * hd + tl.arange(
+        0, pad_hd // 2)[None, :]
+    first_half_k_offsets = tl.arange(0, pad_n_kh)[:, None] * hd + tl.arange(
+        0, pad_hd // 2)[None, :]
+    first_q_mask = (tl.arange(0, pad_n_qh)[:, None] < n_qh) & (tl.arange(
+        0, pad_hd // 2)[None, :] < hd // 2)
+    first_k_mask = (tl.arange(0, pad_n_kh)[:, None] < n_kh) & (tl.arange(
+        0, pad_hd // 2)[None, :] < hd // 2)
+
+    q_tile_1 = tl.load(q_ptr + first_half_q_offsets,
+                       mask=first_q_mask,
+                       other=0).to(sin_row.dtype)
+    k_tile_1 = tl.load(k_ptr + first_half_k_offsets,
+                       mask=first_k_mask,
+                       other=0).to(sin_row.dtype)
+
+    # right half of the head
+    second_half_q_offsets = first_half_q_offsets + (hd // 2)
+    second_half_k_offsets = first_half_k_offsets + (hd // 2)
+    second_q_mask = first_q_mask
+    second_k_mask = first_k_mask
+
+    q_tile_2 = tl.load(q_ptr + second_half_q_offsets,
+                       mask=second_q_mask,
+                       other=0).to(sin_row.dtype)
+    k_tile_2 = tl.load(k_ptr + second_half_k_offsets,
+                       mask=second_k_mask,
+                       other=0).to(sin_row.dtype)
+
+    # y = [x1, x2] * [cos, cos] + [-x2, x1] * [sin, sin]
+    # Since cos and sin are now half-size,
+    # we use the same cos_row and sin_row for both halves
+    new_q_tile_1 = q_tile_1 * cos_row - q_tile_2 * sin_row
+    tl.store(q_ptr + first_half_q_offsets, new_q_tile_1, mask=first_q_mask)
+    new_q_tile_2 = q_tile_2 * cos_row + q_tile_1 * sin_row
+    tl.store(q_ptr + second_half_q_offsets, new_q_tile_2, mask=second_q_mask)
+
+    new_k_tile_1 = k_tile_1 * cos_row - k_tile_2 * sin_row
+    tl.store(k_ptr + first_half_k_offsets, new_k_tile_1, mask=first_k_mask)
+    new_k_tile_2 = k_tile_2 * cos_row + k_tile_1 * sin_row
+    tl.store(k_ptr + second_half_k_offsets, new_k_tile_2, mask=second_k_mask)
+
+
+def triton_mrope(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    mrope_section: list[int],
+    head_size: int,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Qwen2VL mrope kernel.
+
+    Args:
+        query: [num_tokens, num_heads * head_size]
+        key: [num_tokens, num_kv_heads * head_size]
+        cos: [3, num_tokens, head_size //2 ]
+            (T/H/W positions with multimodal inputs)
+        sin: [3, num_tokens, head_size //2 ]
+            (T/H/W positions with multimodal inputs)
+        mrope_section: [t, h, w]
+        head_size: int
+    """
+    n_row, n_q_head_head_dim = q.shape
+    n_q_head = n_q_head_head_dim // head_size
+    n_kv_head = k.shape[1] // head_size
+    pad_hd = triton.next_power_of_2(head_size)
+    pad_n_q_head = triton.next_power_of_2(n_q_head)
+    pad_n_kv_head = triton.next_power_of_2(n_kv_head)
+
+    # ensure tensors passed into the kernel are contiguous.
+    # It will be no-op if they are already contiguous
+    q = q.contiguous()
+    k = k.contiguous()
+    cos = cos.contiguous()
+    sin = sin.contiguous()
+
+    _triton_qwen2vl_mrope_forward[(n_row, )](
+        q,
+        k,
+        cos,
+        sin,
+        n_row,
+        n_q_head,
+        n_kv_head,
+        head_size,
+        pad_n_q_head,
+        pad_n_kv_head,
+        pad_hd,
+        mrope_section[0],
+        mrope_section[1],
+    )
+    return q, k
+
+
 class MRotaryEmbedding(RotaryEmbedding):
     """Rotary Embedding with Multimodal Sections."""
 
@@ -36,11 +199,34 @@ class MRotaryEmbedding(RotaryEmbedding):
         if self.mrope_section:
             assert sum(self.mrope_section) == rotary_dim // 2
 
+        self.use_triton = current_platform.is_cuda_alike()
+
     def forward(
         self,
         positions: torch.Tensor,
         query: torch.Tensor,
         key: Optional[torch.Tensor] = None,
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+        """MRope forward.
+
+        Args:
+            positions:
+                [num_tokens,] (text only) or
+                [3, num_tokens] (T/H/W positions with multimodal inputs)
+            query: [num_tokens, num_heads * head_size]
+            key: [num_tokens, num_kv_heads * head_size]
+        """
+        if self.use_triton:
+            return self.forward_cuda(positions, query, key)
+        else:
+            return self.forward_native(positions, query, key)
+
+    def forward_native(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: Optional[torch.Tensor] = None,
+        offsets: Optional[torch.Tensor] = None,
     ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
         """PyTorch-native implementation equivalent to forward().
 
@@ -88,6 +274,51 @@ class MRotaryEmbedding(RotaryEmbedding):
         key = torch.cat((key_rot, key_pass), dim=-1).reshape(key_shape)
         return query, key
 
+    def forward_cuda(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: Optional[torch.Tensor] = None,
+        offsets: Optional[torch.Tensor] = None,
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+
+        assert positions.ndim == 1 or positions.ndim == 2
+        assert key is not None
+
+        num_tokens = positions.shape[-1]
+        cos_sin = self.cos_sin_cache[positions]
+        cos, sin = cos_sin.chunk(2, dim=-1)
+        query_shape = query.shape
+        key_shape = key.shape
+        if positions.ndim == 2:
+            assert self.mrope_section
+
+            q, k = triton_mrope(
+                query,
+                key,
+                cos,
+                sin,
+                self.mrope_section,
+                self.head_size,
+            )
+
+            return q.reshape(query_shape), k.reshape(key_shape)
+
+        query = query.view(num_tokens, -1, self.head_size)
+        query_rot = query[..., :self.rotary_dim]
+        query_pass = query[..., self.rotary_dim:]
+        query_rot = apply_rotary_emb_dispatch(query_rot, cos, sin,
+                                              self.is_neox_style)
+        query = torch.cat((query_rot, query_pass), dim=-1).reshape(query_shape)
+
+        key = key.view(num_tokens, -1, self.head_size)
+        key_rot = key[..., :self.rotary_dim]
+        key_pass = key[..., self.rotary_dim:]
+        key_rot = apply_rotary_emb_dispatch(key_rot, cos, sin,
+                                            self.is_neox_style)
+        key = torch.cat((key_rot, key_pass), dim=-1).reshape(key_shape)
+        return query, key
+
     @classmethod
     def get_input_positions(
         cls,