diff --git a/sgl-kernel/README.md b/sgl-kernel/README.md
index cd3c0288a..421cf3c69 100644
--- a/sgl-kernel/README.md
+++ b/sgl-kernel/README.md
@@ -7,263 +7,6 @@
 ## Installation
 
 ```bash
-pip3 install sgl-kernel
+# latest version
+pip3 install sgl-kernel --upgrade
 ```
-
-For CUDA 12.4:
-
-```bash
-pip3 install sgl-kernel -i https://docs.sglang.ai/whl/cu124
-```
-
-## Build from source
-
-Development build:
-
-```bash
-make build
-```
-
-Note:
-The `sgl-kernel` is rapidly evolving. If you experience a compilation failure, try using `make rebuild`.
-
-### Build with [ccache](https://github.com/ccache/ccache)
-```bash
-# or `yum install -y ccache`.
-apt-get install -y ccache
-# Building with ccache is enabled when ccache is installed and CCACHE_DIR is set.
-export CCACHE_DIR=/path/to/your/ccache/dir
-export CCACHE_BACKEND=""
-export CCACHE_KEEP_LOCAL_STORAGE="TRUE"
-unset CCACHE_READONLY
-python -m uv build --wheel -Cbuild-dir=build --color=always .
-```
-
-### Configuring CMake Build Options
-Cmake options can be configuring by adding `-Ccmake.define.<option>=<value>` to the `uv build` flags.
-For example, to enable building FP4 kernels, use:
-```bash
-python -m uv build --wheel -Cbuild-dir=build -Ccmake.define.SGL_KERNEL_ENABLE_FP4=1 --color=always .
-```
-See CMakeLists.txt for more options.
-
-### Parallel Build
-
-We highly recommend you build sgl-kernel with Ninja. Ninja can automatically build sgl-kernel in parallel.
-And if you build the sgl-kernel with cmake, you need to add `CMAKE_BUILD_PARALLEL_LEVEL` and limit the
-nvcc threads to a single thread by setting `SGL_KERNEL_COMPILE_THREADS=1` for parallel build like:
-
-```bash
-CMAKE_BUILD_PARALLEL_LEVEL=$(nproc) python -m uv build --wheel -Cbuild-dir=build \
--Ccmake.define.SGL_KERNEL_COMPILE_THREADS=1 --color=always .
-```
-
-### ⚠️ Compilation Issue with `sgl-kernel` and CUDA 12.6
-
-When compiling `sgl-kernel` with FlashAttention on a Hopper GPU using CUDA 12.6, you may encounter a segmentation fault:
-
-```bash
-kernel/build/_deps/repo-flash-attention-src/hopper/instantiations/flash_fwd_hdimall_bf16_paged_softcap_sm90.cu -o CMakeFiles/flash_ops.dir/_deps/repo-flash-attention-src/hopper/instantiations/flash_fwd_hdimall_bf16_paged_softcap_sm90.cu.o
-Segmentation fault (core dumped)
-```
-
-⚠️ **Note**: To ensure that FlashAttention compiles correctly on Hopper GPU Architecture(sm90), it is strongly [recommended](https://github.com/Dao-AILab/flash-attention/issues/1453) to use:
-- nvcc version: 12.6
-- ptxas version: 12.8
-
-**1. Check Current Versions**
-
-Before proceeding, verify your current CUDA tool versions:
-```bash
-nvcc --version
-ptxas --version
-```
-**2. Update ptxas to 12.8 (if needed)**
-
-1. Save the following script to a file (e.g., `update_ptxas.sh`).
-```bash
-#!/usr/bin/env bash
-# Source: https://github.com/Dao-AILab/flash-attention/blob/7ff1b621112ba8b538e2fc6a316f2a6b6f22e518/hopper/setup.py#L404
-set -ex
-
-if [ -z "$1" ]; then
-    echo "Usage: $0 <CUDA_VERSION>"
-    exit 1
-fi
-
-CUDA_VERSION=$1
-
-if awk "BEGIN {exit !("$CUDA_VERSION" >= 12.6 && "$CUDA_VERSION" < 12.8)}"; then
-    NVCC_ARCHIVE_VERSION="12.8.93"
-    NVCC_ARCHIVE_NAME="cuda_nvcc-linux-x86_64-${NVCC_ARCHIVE_VERSION}-archive"
-    NVCC_ARCHIVE_TAR="${NVCC_ARCHIVE_NAME}.tar.xz"
-    NVCC_ARCHIVE_URL="https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvcc/linux-x86_64/${NVCC_ARCHIVE_TAR}"
-
-    wget "$NVCC_ARCHIVE_URL"
-    tar -xf "$NVCC_ARCHIVE_TAR"
-
-    mkdir -p /usr/local/cuda/bin
-    cp "${NVCC_ARCHIVE_NAME}/bin/ptxas" /usr/local/cuda/bin/
-
-    # Clean up temporary files
-    rm -f "${NVCC_ARCHIVE_TAR}"
-    rm -rf "${NVCC_ARCHIVE_NAME}"
-fi
-```
-2. Run the script with your CUDA version as the argument, using `sudo`:
-```bash
-sudo bash update_ptxas.sh 12.6
-# Check the version
-ptxas --version
-```
-
-# Developer Guide
-
-## Development Environment Setup
-
-Use Docker to set up the development environment. See [Docker setup guide](https://github.com/sgl-project/sglang/blob/main/docs/developer_guide/development_guide_using_docker.md#setup-docker-container).
-
-Create and enter development container:
-```bash
-docker run -itd --shm-size 32g --gpus all -v $HOME/.cache:/root/.cache --ipc=host --name sglang_zhyncs lmsysorg/sglang:dev /bin/zsh
-docker exec -it sglang_zhyncs /bin/zsh
-```
-
-## Project Structure
-
-### Dependencies
-
-Third-party libraries:
-
-- [CUTLASS](https://github.com/NVIDIA/cutlass)
-- [FlashInfer](https://github.com/flashinfer-ai/flashinfer)
-- [DeepGEMM](https://github.com/deepseek-ai/DeepGEMM)
-- [FlashAttention](https://github.com/Dao-AILab/flash-attention)
-
-### FlashAttention FYI
-
-  FA3 can fail without a enough shared memory for a some shapes, such as higher hidden_dim or some special cases. Right now, fa3 is supported for sm80/sm87 and sm86/sm89.
-
-  The main different Between sm80/sm87 and sm86/sm89 is the shared memory size. you can follow the link below for more information https://docs.nvidia.com/cuda/cuda-c-programming-guide/#shared-memory-8-x.
-
-  And for sgl-kernel right now, we can build fa3 on sm80/sm86/sm89/sm90a. That means if you use **A100(tested)**/A*0/**L20(tested)**/L40/L40s/**3090(tested)** you can use fa3.
-
-### Kernel Development
-
-Steps to add a new kernel:
-
-1. Implement the kernel in [csrc](https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc)
-2. Expose the interface in [include/sgl_kernel_ops.h](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/include/sgl_kernel_ops.h)
-3. Create torch extension in [csrc/common_extension.cc](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/csrc/common_extension.cc)
-4. Update [CMakeLists.txt](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/CMakeLists.txt) to include new CUDA source
-5. Expose Python interface in [python](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/python/sgl_kernel)
-
-### Development Tips
-
-1. When implementing kernels in [csrc](https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc), only define pure CUDA files and C++ interfaces. If you need to use `Torch::tensor`, use `<torch/all.h>` instead of `<torch/extension.h>`. Using `<torch/extension.h>` will cause compilation errors when using SABI.
-
-2. When creating torch extensions, add the function definition with `m.def`, and device binding with `m.impl`:
-- Using torch.compile need `m.def` with schema, it helps auto capture the custom kernel. Reference: [How to add FakeTensor](https://docs.google.com/document/d/1_W62p8WJOQQUzPsJYa7s701JXt0qf2OfLub2sbkHOaU/edit?tab=t.0#heading=h.ptttacy8y1u9)
-
-- How to write schema: [Schema reference](https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/README.md#func)
-
-   ```cpp
-   // We need def with schema here for torch.compile
-   m.def(
-    "bmm_fp8(Tensor A, Tensor B, Tensor! D, Tensor A_scale, Tensor B_scale, Tensor workspace_buffer, int "
-    "cublas_handle, int cuda_stream) -> ()");
-   m.impl("bmm_fp8", torch::kCUDA, &bmm_fp8);
-   ```
-
-3. When exposing Python interfaces, avoid using kwargs in C++ interface kernels.
-
-    **Avoid this:**
-
-    ```cpp
-    torch.ops.sgl_kernel.apply_rope_pos_ids_cos_sin_cache.default(
-        q=query.view(query.shape[0], -1, head_size),
-        k=key.view(key.shape[0], -1, head_size),
-        q_rope=query.view(query.shape[0], -1, head_size),
-        k_rope=key.view(key.shape[0], -1, head_size),
-        cos_sin_cache=cos_sin_cache,
-        pos_ids=positions.long(),
-        interleave=(not is_neox),
-        cuda_stream=get_cuda_stream(),
-    )
-    ```
-
-    **Use this instead:**
-
-    ```cpp
-    torch.ops.sgl_kernel.apply_rope_pos_ids_cos_sin_cache.default(
-        query.view(query.shape[0], -1, head_size),
-        key.view(key.shape[0], -1, head_size),
-        query.view(query.shape[0], -1, head_size),
-        key.view(key.shape[0], -1, head_size),
-        cos_sin_cache,
-        positions.long(),
-        (not is_neox),
-        get_cuda_stream(),
-    )
-    ```
-
-### Integrating Third-Party Libraries with Data Type Conversion
-
-When integrating new third-party libraries like flash-attention, you may encounter data type compatibility issues between the C++ interface and PyTorch bindings. For example, the third-party code might use `float` or `int` types, while PyTorch requires `double` and `int64_t`.
-
-> The reason we need `double` and `int64_t` in torch binding is that TORCH_LIBRARY handles the `Python-to-C++` conversion process. Python's `float` data type actually corresponds to `double` in C++, while Python's `int` corresponds to `int64_t` in C++.
-
-To address this issue, we provide the `make_pytorch_shim` function in [sgl_kernel_torch_shim](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/include/sgl_kernel_torch_shim.h) that handles data type conversions automatically.
-
-When you need to support new data type conversions, you can easily add conversion functions like this:
-
-```cpp
-// Map `int` -> `int64_t`
-template <>
-struct pytorch_library_compatible_type<int> {
-  using type = int64_t;
-  static int convert_from_type(int64_t arg) {
-    TORCH_CHECK(arg <= std::numeric_limits<int>::max(), "int64_t value is too large to be converted  to int");
-    TORCH_CHECK(arg >= std::numeric_limits<int>::min(), "int64_t value is too small to be converted to int");
-    return arg;
-  }
-};
-```
-
-To use this with your library functions, simply wrap them with make_pytorch_shim:
-
-```cpp
-/*
- * From flash-attention
- */
- m.impl("fwd", torch::kCUDA, make_pytorch_shim(&mha_fwd));
-```
-
-### Testing & Benchmarking
-
-1. Add pytest tests in [tests/](https://github.com/sgl-project/sglang/tree/main/sgl-kernel/tests), if you need to skip some test, please use `@pytest.mark.skipif`
-
-```python
-@pytest.mark.skipif(
-    skip_condition, reason="Nvfp4 Requires compute capability of 10 or above."
-)
-```
-
-2. Add benchmarks using [triton benchmark](https://triton-lang.org/main/python-api/generated/triton.testing.Benchmark.html) in [benchmark/](https://github.com/sgl-project/sglang/tree/main/sgl-kernel/benchmark)
-
-   **We recommend using `triton.testing.do_bench_cudagraph` for kernel benchmarking**:
-
-   Compared to `triton.testing.do_bench`, `do_bench_cudagraph` provides:
-   - Reduced CPU overhead impact for more accurate kernel performance measurements
-   - Incorporation of PDL (Programmatic Dependent Launch) effects into individual kernel results
-   - More realistic performance data on PDL-supported architectures (SM >= 90)
-
-3. Run test suite
-
-### FAQ
-
-- When encountering this error while compiling using ccache: `ImportError: /usr/local/lib/python3.10/dist-packages/sgl_kernel/common_ops.abi3.so: undefined symbol: _ZN3c108ListType3getERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEENS_4Type24SingletonOrSharedTypePtrIS9_EE`, please modify the last command as follows to resolve it: `python3 -m uv build --wheel -Cbuild-dir=build . --color=always --no-build-isolation` .
-
-### Release new version
-
-Update version in [pyproject.toml](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/pyproject.toml) and [version.py](https://github.com/sgl-project/sglang/blob/main/sgl-kernel/python/sgl_kernel/version.py)