diff --git a/python/examples/study/with_blocking_krn.py b/python/examples/study/with_blocking_krn.py
deleted file mode 100644
index ff02bd3..0000000
--- a/python/examples/study/with_blocking_krn.py
+++ /dev/null
@@ -1,76 +0,0 @@
-# Copyright 2025 NVIDIA Corporation
-#
-#  Licensed under the Apache License, Version 2.0 with the LLVM exception
-#  (the "License"); you may not use this file except in compliance with
-#  the License.
-#
-#  You may obtain a copy of the License at
-#
-#      http://llvm.org/foundation/relicensing/LICENSE.txt
-#
-#  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.
-
-import sys
-
-import cuda.bench as bench
-import numpy as np
-from numba import cuda
-
-
-def as_cuda_stream(cs: bench.CudaStream) -> cuda.cudadrv.driver.Stream:
-    return cuda.external_stream(cs.addressof())
-
-
-def make_throughput_kernel(items_per_thread: int) -> cuda.dispatcher.CUDADispatcher:
-    @cuda.jit
-    def kernel(stride: np.uintp, elements: np.uintp, in_arr, out_arr):
-        tid = cuda.grid(1)
-        step = cuda.gridDim.x * cuda.blockDim.x
-        for i in range(stride * tid, stride * elements, stride * step):
-            for j in range(items_per_thread):
-                read_id = (items_per_thread * i + j) % elements
-                write_id = tid + j * elements
-                out_arr[write_id] = in_arr[read_id]
-
-    return kernel
-
-
-def throughput_bench(state: bench.State) -> None:
-    stride = state.get_int64("Stride")
-    ipt = state.get_int64("ItemsPerThread")
-
-    nbytes = 128 * 1024 * 1024
-    elements = nbytes // np.dtype(np.int32).itemsize
-
-    alloc_stream = as_cuda_stream(state.get_stream())
-    inp_arr = cuda.device_array(elements, dtype=np.int32, stream=alloc_stream)
-    out_arr = cuda.device_array(elements * ipt, dtype=np.int32, stream=alloc_stream)
-
-    state.add_element_count(elements, column_name="Elements")
-    state.add_global_memory_reads(inp_arr.nbytes, column_name="Datasize")
-    state.add_global_memory_writes(inp_arr.nbytes)
-
-    threads_per_block = 256
-    blocks_in_grid = (elements + threads_per_block - 1) // threads_per_block
-
-    krn = make_throughput_kernel(ipt)
-
-    def launcher(launch: bench.Launch):
-        exec_stream = as_cuda_stream(launch.get_stream())
-        krn[blocks_in_grid, threads_per_block, exec_stream, 0](
-            stride, elements, inp_arr, out_arr
-        )
-
-    state.exec(launcher)
-
-
-if __name__ == "__main__":
-    b = bench.register(throughput_bench)
-    b.add_int64_axis("Stride", [1, 2, 4])
-    b.add_int64_axis("ItemsPerThread", [1, 2, 3, 4])
-
-    bench.run_all_benchmarks(sys.argv)
diff --git a/python/examples/study/without_blocking_krn.py b/python/examples/study/without_blocking_krn.py
deleted file mode 100644
index 1a22256..0000000
--- a/python/examples/study/without_blocking_krn.py
+++ /dev/null
@@ -1,76 +0,0 @@
-# Copyright 2025 NVIDIA Corporation
-#
-#  Licensed under the Apache License, Version 2.0 with the LLVM exception
-#  (the "License"); you may not use this file except in compliance with
-#  the License.
-#
-#  You may obtain a copy of the License at
-#
-#      http://llvm.org/foundation/relicensing/LICENSE.txt
-#
-#  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.
-
-import sys
-
-import cuda.bench as bench
-import numpy as np
-from numba import cuda
-
-
-def as_cuda_stream(cs: bench.CudaStream) -> cuda.cudadrv.driver.Stream:
-    return cuda.external_stream(cs.addressof())
-
-
-def make_throughput_kernel(items_per_thread: int) -> cuda.dispatcher.CUDADispatcher:
-    @cuda.jit
-    def kernel(stride: np.uintp, elements: np.uintp, in_arr, out_arr):
-        tid = cuda.grid(1)
-        step = cuda.gridDim.x * cuda.blockDim.x
-        for i in range(stride * tid, stride * elements, stride * step):
-            for j in range(items_per_thread):
-                read_id = (items_per_thread * i + j) % elements
-                write_id = tid + j * elements
-                out_arr[write_id] = in_arr[read_id]
-
-    return kernel
-
-
-def throughput_bench(state: bench.State) -> None:
-    stride = state.get_int64("Stride")
-    ipt = state.get_int64("ItemsPerThread")
-
-    nbytes = 128 * 1024 * 1024
-    elements = nbytes // np.dtype(np.int32).itemsize
-
-    alloc_stream = as_cuda_stream(state.get_stream())
-    inp_arr = cuda.device_array(elements, dtype=np.int32, stream=alloc_stream)
-    out_arr = cuda.device_array(elements * ipt, dtype=np.int32, stream=alloc_stream)
-
-    state.add_element_count(elements, column_name="Elements")
-    state.add_global_memory_reads(inp_arr.nbytes, column_name="Datasize")
-    state.add_global_memory_writes(inp_arr.nbytes)
-
-    threads_per_block = 256
-    blocks_in_grid = (elements + threads_per_block - 1) // threads_per_block
-
-    krn = make_throughput_kernel(ipt)
-
-    def launcher(launch: bench.Launch):
-        exec_stream = as_cuda_stream(launch.get_stream())
-        krn[blocks_in_grid, threads_per_block, exec_stream, 0](
-            stride, elements, inp_arr, out_arr
-        )
-
-    state.exec(launcher, sync=True)
-
-
-if __name__ == "__main__":
-    b = bench.register(throughput_bench)
-    b.add_int64_axis("Stride", [1, 2, 4])
-    b.add_int64_axis("ItemsPerThread", [1, 2, 3, 4])
-
-    bench.run_all_benchmarks(sys.argv)