diff --git a/python/sglang/srt/layers/multimodal.py b/python/sglang/srt/layers/multimodal.py
new file mode 100644
index 000000000..ad313a31f
--- /dev/null
+++ b/python/sglang/srt/layers/multimodal.py
@@ -0,0 +1,70 @@
+# Copyright 2023-2024 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Logits processing."""
+
+import torch
+import triton
+import triton.language as tl
+
+
+@triton.jit
+def hash_kernel(
+    input_ptr,
+    output_ptr,
+    n_elements,
+    BLOCK_SIZE: tl.constexpr,
+    PRIME: tl.constexpr,
+    XCONST: tl.constexpr,
+):
+    pid = tl.program_id(axis=0)
+    block_start = pid * BLOCK_SIZE
+    offsets = block_start + tl.arange(0, BLOCK_SIZE)
+    mask = offsets < n_elements
+
+    data = tl.load(input_ptr + offsets, mask=mask, other=0)
+    mixed = data ^ (offsets + XCONST)
+    hash_val = mixed * PRIME
+    hash_val = hash_val ^ (hash_val >> 16)
+    hash_val = hash_val * (PRIME ^ XCONST)
+    hash_val = hash_val ^ (hash_val >> 13)
+
+    tl.store(output_ptr + offsets, hash_val, mask=mask)
+
+
+PRIME_1 = -(11400714785074694791 ^ 0xFFFFFFFFFFFFFFFF) - 1
+PRIME_2 = -(14029467366897019727 ^ 0xFFFFFFFFFFFFFFFF) - 1
+
+
+def gpu_tensor_hash(tensor: torch.Tensor) -> int:
+    assert tensor.is_cuda
+    tensor = tensor.contiguous().view(torch.int32)
+    n = tensor.numel()
+    BLOCK_SIZE = 1024
+    grid = (triton.cdiv(n, BLOCK_SIZE),)
+
+    intermediate_hashes = torch.empty(n, dtype=torch.int32, device=tensor.device)
+
+    hash_kernel[grid](
+        tensor,
+        intermediate_hashes,
+        n,
+        BLOCK_SIZE=BLOCK_SIZE,
+        PRIME=PRIME_1,
+        XCONST=PRIME_2,
+    )
+
+    # TODO: threads can't be synced on triton kernel
+    final_hash = intermediate_hashes.sum().item()
+
+    return final_hash
diff --git a/python/sglang/srt/managers/schedule_batch.py b/python/sglang/srt/managers/schedule_batch.py
index 836335136..10f91ed20 100644
--- a/python/sglang/srt/managers/schedule_batch.py
+++ b/python/sglang/srt/managers/schedule_batch.py
@@ -49,6 +49,7 @@ from sglang.srt.configs.model_config import ModelConfig
 from sglang.srt.constrained.base_grammar_backend import BaseGrammarObject
 from sglang.srt.disaggregation.base import BaseKVSender
 from sglang.srt.disaggregation.decode import ScheduleBatchDisaggregationDecodeMixin
+from sglang.srt.layers.multimodal import gpu_tensor_hash
 from sglang.srt.mem_cache.base_prefix_cache import BasePrefixCache
 from sglang.srt.mem_cache.chunk_cache import ChunkCache
 from sglang.srt.mem_cache.memory_pool import ReqToTokenPool, TokenToKVPoolAllocator
@@ -222,7 +223,8 @@ class MultimodalDataItem:
                     for x in tensor_list
                 ]
                 tensor = torch.concat(tensor_list)
-
+            if tensor.is_cuda:
+                return gpu_tensor_hash(tensor)
             tensor = tensor.detach().contiguous()
 
             if tensor.dtype == torch.bfloat16: