diff --git a/ascend/test/sglang/v0.4.8/test_create_chunked_prefix_cache_kv_indices.py b/ascend/test/sglang/v0.4.8/test_create_chunked_prefix_cache_kv_indices.py
new file mode 100644
index 0000000000000000000000000000000000000000..19e7782ddb8409c059aff480e8b6abf5932c269b
--- /dev/null
+++ b/ascend/test/sglang/v0.4.8/test_create_chunked_prefix_cache_kv_indices.py
@@ -0,0 +1,71 @@
+import sys
+import pytest
+import triton
+import torch
+import triton.language as tl
+
+sys.path.append("..")
+import test_common
+
+
+# source: python\sglang\srt\model_executor\forward_batch_info.py
+@triton.jit
+def create_chunked_prefix_cache_kv_indices(
+    req_to_token_ptr,  # (max_batch, max_context_len,)
+    req_pool_indices_ptr,  # (batch_size,)
+    chunk_start_idx_ptr,  # (batch_size,) 
+    chunk_seq_lens_ptr,  # (batch_size,)
+    chunk_cu_seq_lens_ptr,  # (batch_size + 1,)
+    chunk_kv_indices_ptr,  # (num_chunk_tokens,)
+    req_to_token_ptr_stride: tl.constexpr,
+):
+    BLOCK_SIZE: tl.constexpr = 512
+    pid = tl.program_id(axis=0)
+
+    # find the req pool idx, this is for batch to token
+    req_pool_index = tl.load(req_pool_indices_ptr + pid)
+    chunk_kv_indices_offset = tl.load(chunk_cu_seq_lens_ptr + pid)
+
+    # get the token position of current chunk
+    chunk_start_pos = tl.load(chunk_start_idx_ptr + pid).to(tl.int32)
+    chunk_seq_len = tl.load(chunk_seq_lens_ptr + pid).to(tl.int32)
+
+    num_loop = tl.cdiv(chunk_seq_len, BLOCK_SIZE)
+    for i in range(num_loop):
+        offset = tl.arrange(0, BLOCK_SIZE) + i * BLOCK_SIZE
+        mask = offset < chunk_seq_len
+        data = tl.load(
+            req_to_token_ptr
+            + req_pool_index * req_to_token_ptr_stride
+            + chunk_start_pos
+            + offset,
+            mask=mask,
+        )
+        tl.store(
+            chunk_kv_indices_ptr + chunk_kv_indices_offset + offset, data, mask=mask
+        )
+
+
+
+def test_context_fwd_kernel(ptfile_path):
+    try:
+        data = torch.load(ptfile_path, map_location=torch.device('cpu'), weights_only=False)
+    except Exception as e:
+        pytest.fail(f"load file {ptfile_path} failed: {str(e)}")
+
+    # ptfile format:
+    # [input_data] (dict):
+    #     key : value
+    # [gpu_output] (dict):
+    #     key : value
+    # [grid] :
+    #     (1,)
+    input_data = test_common.convert_tensor_with_device_type(data["input_data"], device_type='npu')
+
+    create_chunked_prefix_cache_kv_indices[data["grid"]](**input_data)
+
+    # compare the results of GPU and NPU.
+    try:
+        test_common.compare_data_precision(data["gpu_output"], input_data, device_type='cpu')
+    except ValueError as e:
+        pytest.fail(f"The testcase failed")