diff --git a/README.md b/README.md
index 7f3e6b4c6..2e5e4b166 100644
--- a/README.md
+++ b/README.md
@@ -23,7 +23,7 @@ The extension can be loaded as a Python module for Python programs or linked as
 
 In the current technological landscape, Generative AI (GenAI) workloads and models have gained widespread attention and popularity. Large Language Models (LLMs) have emerged as the dominant models driving these GenAI applications. Starting from 2.1.0, specific optimizations for certain LLM models are introduced in the Intel® Extension for PyTorch\*. Check [LLM optimizations CPU](./examples/cpu/inference/python/llm) and [LLM optimizations GPU](./examples/gpu/llm) for details.
 
-### Optimized Model List 
+### Validated Model List 
 
 #### LLM Inference
 
diff --git a/csrc/gpu/utils/LogImpl.cpp b/csrc/gpu/utils/LogImpl.cpp
index a7a9abed1..ebc330f79 100644
--- a/csrc/gpu/utils/LogImpl.cpp
+++ b/csrc/gpu/utils/LogImpl.cpp
@@ -15,7 +15,7 @@ spdlog::level::level_enum get_log_level_from_int(int level) {
     return spdlog::level::critical;
   } else {
     throw std::runtime_error(
-        "USING error log level for IPEX_LOGGING, log level should be -1 to 5, but met " +
+        "USING error log level for IPEX_LOG, log level should be -1 to 5, but met " +
         std::string{level});
   }
 }
@@ -231,7 +231,7 @@ void EventLogger::print_result(int log_level) {
     if (this->message_queue.size() >= 2) {
       auto next_time = this->message_queue.front().timestamp;
       auto next_step = this->message_queue.front().step_id;
-      // inside IPEX_LOGGING we are using nanoseconds, 1ns = 0.001us, cast to us
+      // inside IPEX_LOG we are using nanoseconds, 1ns = 0.001us, cast to us
       // here
       auto time_step = static_cast<float>((next_time - this_time) / 1000);
       log_result_with_args(
@@ -283,3 +283,4 @@ void BasicLogger::update_logger() {
   logger->set_pattern("[%c %z] [%l] [thread %t] %v");
   spdlog::set_default_logger(logger);
 }
+
diff --git a/docs/tutorials/api_doc.rst b/docs/tutorials/api_doc.rst
index d755dcd96..3d35dd00e 100644
--- a/docs/tutorials/api_doc.rst
+++ b/docs/tutorials/api_doc.rst
@@ -6,66 +6,12 @@ General
 
 .. currentmodule:: intel_extension_for_pytorch
 .. autofunction:: optimize
-.. autofunction:: optimize_transformers
+.. currentmodule:: intel_extension_for_pytorch.llm
+.. autofunction:: optimize
+.. currentmodule:: intel_extension_for_pytorch
 .. autofunction:: get_fp32_math_mode
 .. autofunction:: set_fp32_math_mode
 
-
-Miscellaneous
-=============
-
-.. currentmodule:: intel_extension_for_pytorch.xpu
-.. StreamContext
-.. can_device_access_peer
-.. current_blas_handle
-.. autofunction:: current_device
-.. autofunction:: current_stream
-.. default_stream
-.. autoclass:: device
-.. autofunction:: device_count
-.. autoclass:: device_of
-.. autofunction:: get_device_name
-.. autofunction:: get_device_properties
-.. get_gencode_flags
-.. get_sync_debug_mode
-.. autofunction:: init
-.. ipc_collect
-.. autofunction:: is_available
-.. autofunction:: is_initialized
-.. memory_usage
-.. autofunction:: set_device
-.. set_stream
-.. autofunction:: stream
-.. autofunction:: synchronize
-
-.. currentmodule:: intel_extension_for_pytorch.xpu.fp8.fp8
-.. autofunction:: fp8_autocast
-
-
-Random Number Generator
-=======================
-
-.. currentmodule:: intel_extension_for_pytorch.xpu
-.. autofunction:: get_rng_state
-.. autofunction:: get_rng_state_all
-.. autofunction:: set_rng_state
-.. autofunction:: set_rng_state_all
-.. autofunction:: manual_seed
-.. autofunction:: manual_seed_all
-.. autofunction:: seed
-.. autofunction:: seed_all
-.. autofunction:: initial_seed
-
-Streams and events
-==================
-
-.. currentmodule:: intel_extension_for_pytorch.xpu
-.. autoclass:: Stream
-    :members: 
-.. ExternalStream
-.. autoclass:: Event
-    :members: 
-
 Memory management
 =================
 
@@ -92,9 +38,17 @@ Memory management
 .. autofunction:: memory_stats_as_nested_dict
 .. autofunction:: reset_accumulated_memory_stats
 
+
+Quantization
+============
+
+.. currentmodule:: intel_extension_for_pytorch.quantization.fp8
+.. autofunction:: fp8_autocast
+
+
 C++ API
 =======
 
-.. doxygenenum:: xpu::FP32_MATH_MODE
+.. doxygenenum:: torch_ipex::xpu::FP32_MATH_MODE
 
-.. doxygenfunction:: xpu::set_fp32_math_mode
+.. doxygenfunction:: torch_ipex::xpu::set_fp32_math_mode
diff --git a/docs/tutorials/features.rst b/docs/tutorials/features.rst
index b4687da34..597629675 100644
--- a/docs/tutorials/features.rst
+++ b/docs/tutorials/features.rst
@@ -137,19 +137,6 @@ For more detailed information, check `torch.compile for GPU <features/torch_comp
 
    features/torch_compile_gpu
 
-Simple Trace Tool (Prototype)
------------------------------
-
-Simple Trace is a built-in debugging tool that lets you control printing out the call stack for a piece of code. Once enabled, it can automatically print out verbose messages of called operators in a stack format with indenting to distinguish the context. 
-
-For more detailed information, check `Simple Trace Tool <features/simple_trace.md>`_.
-
-.. toctree::
-   :hidden:
-   :maxdepth: 1
-
-   features/simple_trace
-
 Kineto Supported Profiler Tool (Prototype)
 ------------------------------------------
 
@@ -178,13 +165,13 @@ For more detailed information, check `Compute Engine <features/compute_engine.md
    features/compute_engine
 
 
-``IPEX_LOGGING`` (Prototype feature for debug)
-----------------------------------------------
+``IPEX_LOG`` (Prototype feature for debug)
+------------------------------------------
 
 
-``IPEX_LOGGING`` provides the capability to log verbose information from Intel® Extension for PyTorch\* . Please use ``IPEX_LOGGING`` to get the log information or trace the execution from Intel® Extension for PyTorch\*. Please continue using PyTorch\* macros such as ``TORCH_CHECK``, ``TORCH_ERROR``, etc. to get the log information from PyTorch\*.
+``IPEX_LOG`` provides the capability to log verbose information from Intel® Extension for PyTorch\* . Please use ``IPEX_LOG`` to get the log information or trace the execution from Intel® Extension for PyTorch\*. Please continue using PyTorch\* macros such as ``TORCH_CHECK``, ``TORCH_ERROR``, etc. to get the log information from PyTorch\*.
 
-For more detailed information, check `IPEX_LOGGING <features/ipex_log.md>`_.
+For more detailed information, check `IPEX_LOG <features/ipex_log.md>`_.
 
 .. toctree::
    :hidden:
@@ -193,3 +180,4 @@ For more detailed information, check `IPEX_LOGGING <features/ipex_log.md>`_.
    features/ipex_log
 
 
+
diff --git a/docs/tutorials/features/ipex_log.md b/docs/tutorials/features/ipex_log.md
index b872e4e46..1e468eb35 100644
--- a/docs/tutorials/features/ipex_log.md
+++ b/docs/tutorials/features/ipex_log.md
@@ -1,11 +1,11 @@
-`IPEX_LOGGING` (Prototype)
+`IPEX_LOG` (Prototype)
 ==========================
 
 ## Introduction
 
-`IPEX_LOGGING` provides the capability to log verbose information from Intel® Extension for PyTorch\* . Please use `IPEX_LOGGING` to get the log information or trace the execution from Intel® Extension for PyTorch\*. Please continue using PyTorch\* macros such as `TORCH_CHECK`, `TORCH_ERROR`, etc. to get the log information from PyTorch\*.
+`IPEX_LOG` provides the capability to log verbose information from Intel® Extension for PyTorch\* . Please use `IPEX_LOG` to get the log information or trace the execution from Intel® Extension for PyTorch\*. Please continue using PyTorch\* macros such as `TORCH_CHECK`, `TORCH_ERROR`, etc. to get the log information from PyTorch\*.
 
-## `IPEX_LOGGING` Definition
+## `IPEX_LOG` Definition
 ### Log Level
 The supported log levels are defined as follows, default log level is `DISABLED`:
 
@@ -81,3 +81,4 @@ Use `torch.xpu.set_log_level(0)` to get logs to replace the previous usage in `I
 
 ## Replace `IPEX_VERBOSE`
 Use `torch.xpu.set_log_level(1)` to get logs to replace the previous usage in `IPEX_VERBOSE`.
+
diff --git a/docs/tutorials/features/simple_trace.md b/docs/tutorials/features/simple_trace.md
index aa73f89dc..92671e646 100644
--- a/docs/tutorials/features/simple_trace.md
+++ b/docs/tutorials/features/simple_trace.md
@@ -1,5 +1,5 @@
-Simple Trace Tool (Prototype)
-=============================
+Simple Trace Tool (Deprecated)
+==============================
 
 ## Introduction
 
diff --git a/docs/tutorials/getting_started.md b/docs/tutorials/getting_started.md
index d1895c615..d96d2fe7c 100644
--- a/docs/tutorials/getting_started.md
+++ b/docs/tutorials/getting_started.md
@@ -32,9 +32,9 @@ model = ipex.optimize(model, dtype=dtype)
 ########## FP32 ############
 with torch.no_grad():
 ####### BF16 on CPU ########
-with torch.no_grad(), with torch.cpu.amp.autocast():
+with torch.no_grad(), torch.cpu.amp.autocast():
 ##### BF16/FP16 on GPU #####
-with torch.no_grad(), with torch.xpu.amp.autocast(enabled=True, dtype=dtype, cache_enabled=False):
+with torch.no_grad(), torch.xpu.amp.autocast(enabled=True, dtype=dtype, cache_enabled=False):
 ############################
   ###### Torchscript #######
   model = torch.jit.trace(model, data)
@@ -49,13 +49,14 @@ More examples, including training and usage of low precision data types are avai
 
 ## Execution
 
-Execution requires an active Intel® oneAPI environment. Suppose you have the Intel® oneAPI Base Toolkit installed in `/opt/intel/oneapi` directory, activating the environment is as simple as sourcing its environment activation bash scripts.
-
 There are some environment variables in runtime that can be used to configure executions on GPU. Please check [Advanced Configuration](./features/advanced_configuration.html#runtime-configuration) for more detailed information.
 
+Set `OCL_ICD_VENDORS` with default path `/etc/OpenCL/vendors`.
+Set `CCL_ROOT` if you are using multi-GPU.
+
 ```bash
-source /opt/intel/oneapi/compiler/latest/env/vars.sh
-source /opt/intel/oneapi/mkl/latest/env/vars.sh
+export OCL_ICD_VENDORS=/etc/OpenCL/vendors
+export CCL_ROOT=${CONDA_PREFIX} 
 python <script>
 ```
 
diff --git a/docs/tutorials/known_issues.md b/docs/tutorials/known_issues.md
index 8cefe739f..b666f4715 100644
--- a/docs/tutorials/known_issues.md
+++ b/docs/tutorials/known_issues.md
@@ -4,15 +4,15 @@ Troubleshooting
 ## General Usage
 
 - **Problem**: FP64 data type is unsupported on current platform.
-  - **Cause**: FP64 is not natively supported by the [Intel® Data Center GPU Flex Series](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/data-center-gpu/flex-series/overview.html) and [Intel® Arc™ A-Series Graphics](https://www.intel.com/content/www/us/en/products/details/discrete-gpus/arc.html) platforms. 
+  - **Cause**: FP64 is not natively supported by the [Intel® Data Center GPU Flex Series](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/data-center-gpu/flex-series/overview.html) and [Intel® Arc™ A-Series Graphics](https://www.intel.com/content/www/us/en/products/details/discrete-gpus/arc.html) platforms.
     If you run any AI workload on that platform and receive this error message, it means a kernel requires FP64 instructions that are not supported and the execution is stopped.
-- **Problem**: Runtime error `invalid device pointer` if `import horovod.torch as hvd` before `import intel_extension_for_pytorch`
+- **Problem**: Runtime error `invalid device pointer` if `import horovod.torch as hvd` before `import intel_extension_for_pytorch`.
   - **Cause**: Intel® Optimization for Horovod\* uses utilities provided by Intel® Extension for PyTorch\*. The improper import order causes Intel® Extension for PyTorch\* to be unloaded before Intel®
     Optimization for Horovod\* at the end of the execution and triggers this error.
   - **Solution**: Do `import intel_extension_for_pytorch` before `import horovod.torch as hvd`.
 - **Problem**: Number of dpcpp devices should be greater than zero.
-  - **Cause**: If you use Intel® Extension for PyTorch* in a conda environment, you might encounter this error. Conda also ships the libstdc++.so dynamic library file that may conflict with the one shipped
-    in the OS. 
+  - **Cause**: If you use Intel® Extension for PyTorch\* in a conda environment, you might encounter this error. Conda also ships the libstdc++.so dynamic library file that may conflict with the one shipped
+    in the OS.
   - **Solution**: Export the `libstdc++.so` file path in the OS to an environment variable `LD_PRELOAD`.
 - **Problem**: Symbol undefined caused by `_GLIBCXX_USE_CXX11_ABI`.
     ```bash
@@ -20,18 +20,14 @@ Troubleshooting
     ```
   - **Cause**: DPC++ does not support `_GLIBCXX_USE_CXX11_ABI=0`, Intel® Extension for PyTorch\* is always compiled with `_GLIBCXX_USE_CXX11_ABI=1`. This symbol undefined issue appears when PyTorch\* is
     compiled with `_GLIBCXX_USE_CXX11_ABI=0`.
-  - **Solution**: Pass `export GLIBCXX_USE_CXX11_ABI=1` and compile PyTorch\* with particular compiler which supports `_GLIBCXX_USE_CXX11_ABI=1`. We recommend using prebuilt wheels 
+  - **Solution**: Pass `export GLIBCXX_USE_CXX11_ABI=1` and compile PyTorch\* with particular compiler which supports `_GLIBCXX_USE_CXX11_ABI=1`. We recommend using prebuilt wheels
     in [download server](https:// developer.intel.com/ipex-whl-stable-xpu) to avoid this issue.
-- **Problem**: Bad termination after AI model execution finishes when using Intel MPI.
-  - **Cause**: This is a random issue when the AI model (e.g. RN50 training) execution finishes in an Intel MPI environment. It is not user-friendly as the model execution ends ungracefully. It has been fixed in PyTorch* 2.3 ([#116312](https://github.com/pytorch/pytorch/commit/f657b2b1f8f35aa6ee199c4690d38a2b460387ae)). 
-  - **Solution**: Add `dist.destroy_process_group()` during the cleanup stage in the model script, as described 
-    in [Getting Started with Distributed Data Parallel](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html), before Intel® Extension for PyTorch* supports PyTorch* 2.3.
 - **Problem**: `-997 runtime error` when running some AI models on Intel® Arc™ A-Series GPUs.
-  - **Cause**:  Some of the `-997 runtime error` are actually out-of-memory errors. As Intel® Arc™ A-Series GPUs have less device memory than Intel® Data Center GPU Flex Series 170 and Intel® Data Center GPU 
+  - **Cause**:  Some of the `-997 runtime error` are actually out-of-memory errors. As Intel® Arc™ A-Series GPUs have less device memory than Intel® Data Center GPU Flex Series 170 and Intel® Data Center GPU
     Max  Series, running some AI models on them may trigger out-of-memory errors and cause them to report failure such as `-997 runtime error` most likely. This is expected. Memory usage optimization is a work in progress to allow Intel® Arc™ A-Series GPUs to support more AI models.
 - **Problem**: Building from source for Intel® Arc™ A-Series GPUs fails on WSL2 without any error thrown.
   - **Cause**: Your system probably does not have enough RAM, so Linux kernel's Out-of-memory killer was invoked. You can verify this by running `dmesg` on bash (WSL2 terminal).
-  - **Solution**: If the OOM killer had indeed killed the build process, then you can try increasing the swap-size of WSL2, and/or decreasing the number of parallel build jobs with the environment 
+  - **Solution**: If the OOM killer had indeed killed the build process, then you can try increasing the swap-size of WSL2, and/or decreasing the number of parallel build jobs with the environment
     variable `MAX_JOBS` (by default, it's equal to the number of logical CPU cores. So, setting `MAX_JOBS` to 1 is a very conservative approach that would slow things down a lot).
 - **Problem**: Some workloads terminate with an error `CL_DEVICE_NOT_FOUND` after some time on WSL2.
   - **Cause**:  This issue is due to the [TDR feature](https://learn.microsoft.com/en-us/windows-hardware/drivers/display/tdr-registry-keys#tdrdelay) on Windows.
@@ -39,9 +35,9 @@ Troubleshooting
 - **Problem**: Random bad termination after AI model convergence test (>24 hours) finishes.
   - **Cause**: This is a random issue when some AI model convergence test execution finishes. It is not user-friendly as the model execution ends ungracefully.
   - **Solution**: Kill the process after the convergence test finished, or use checkpoints to divide the convergence test into several phases and execute separately.
-- **Problem**: Random instability issues such as page fault or atomic access violation when executing LLM inference workloads on Intel® Data Center GPU Max series cards.
-  - **Cause**:  This issue is reported on LTS driver [803.29](https://dgpu-docs.intel.com/releases/LTS_803.29_20240131.html). The root cause is under investigation.
-  - **Solution**: Use active rolling stable release driver [775.20](https://dgpu-docs.intel.com/releases/stable_775_20_20231219.html) or latest driver version to workaround.
+- **Problem**: Runtime error `munmap_chunk(): invalid pointer` when executing some scaling LLM workloads on Intel® Data Center GPU Max Series platform
+  - **Cause**: Users targeting GPU use, must set the environment variable ‘FI_HMEM=system’ to disable GPU support in underlying libfabric as Intel® MPI Library 2021.13.1 will offload the GPU support instead. This avoids a potential bug in libfabric GPU initialization.
+  - **Solution**: Set the environment variable ‘FI_HMEM=system’ to workaround this issue when encounter.
 
 ## Library Dependencies
 
@@ -54,7 +50,7 @@ Troubleshooting
   /usr/bin/ld: cannot find -lmkl_tbb_thread
   dpcpp: error: linker command failed with exit code 1 (use -v to see invocation)
   ```
-  
+
   - **Cause**: When PyTorch\* is built with oneMKL library and Intel® Extension for PyTorch\* is built without MKL library, this linker issue may occur.
   - **Solution**: Resolve the issue by setting:
 
@@ -66,8 +62,8 @@ Troubleshooting
    Then clean build Intel® Extension for PyTorch\*.
 
 - **Problem**: Undefined symbol: `mkl_lapack_dspevd`. Intel MKL FATAL ERROR: cannot load `libmkl_vml_avx512.so.2` or `libmkl_vml_def.so.2.
-  - **Cause**: This issue may occur when Intel® Extension for PyTorch\* is built with oneMKL library and PyTorch\* is not build with any MKL library. The oneMKL kernel may run into CPU backend incorrectly 
-    and trigger this issue. 
+  - **Cause**: This issue may occur when Intel® Extension for PyTorch\* is built with oneMKL library and PyTorch\* is not build with any MKL library. The oneMKL kernel may run into CPU backend incorrectly
+    and trigger this issue.
   - **Solution**: Resolve the issue by installing the oneMKL library from conda:
 
     ```bash
@@ -87,14 +83,30 @@ Troubleshooting
 
     If you continue seeing similar issues for other shared object files, add the corresponding files under `${MKL_DPCPP_ROOT}/lib/intel64/` by `LD_PRELOAD`. Note that the suffix of the libraries may change (e.g. from .1 to .2), if more than one oneMKL library is installed on the system.
 
+- **Problem**: RuntimeError: could not create an engine.
+  - **Cause**: `OCL_ICD_VENDORS` path is wrongly set when activate a exist conda environment.
+  - **Solution**: `export OCL_ICD_VENDORS=/etc/OpenCL/vendors` after `conda activate`
+
+- **Problem**: If you encounter issues related to CCL environment variable configuration when running distributed tasks.
+  - **Cause**: `CCL_ROOT` path is wrongly set.
+  - **Solution**: `export CCL_ROOT=${CONDA_PREFIX}`
 
-## Performance Issue 
+- **Problem**: If you encounter issues related to MPI environment variable configuration when running distributed tasks.
+  - **Cause**: MPI environment variable configuration not correct.
+  - **Solution**: `conda deactivate` and then `conda activate` to activate the correct MPI environment variable automatically.
+
+    ```
+    conda deactivate
+    conda activate
+    export OCL_ICD_VENDORS=/etc/OpenCL/vendors
+    ```
+
+## Performance Issue
 
 - **Problem**: Extended durations for data transfers from the host system to the device (H2D) and from the device back to the host system (D2H).
   - **Cause**: Absence of certain Dynamic Kernel Module Support (DKMS) packages on Ubuntu 22.04 or earlier versions.
   - **Solution**: For those running Ubuntu 22.04 or below, it's crucial to follow all the recommended installation procedures, including those labeled as [optional](https://dgpu-docs.intel.com/driver/client/overview.html#optional-out-of-tree-kernel-mode-driver-install). These steps are likely necessary to install the missing DKMS packages and ensure your system is functioning optimally. The Kernel Mode Driver (KMD) package that addresses this issue has been integrated into the Linux kernel for Ubuntu 23.04 and subsequent releases.
 
-
 ## Unit Test
 
 - Unit test failures on Intel® Data Center GPU Flex Series 170
diff --git a/docs/tutorials/llm.rst b/docs/tutorials/llm.rst
index 2b42fecec..c7c427160 100644
--- a/docs/tutorials/llm.rst
+++ b/docs/tutorials/llm.rst
@@ -5,7 +5,7 @@ In the current technological landscape, Generative AI (GenAI) workloads and mode
 The MultiHeadAttention and FeedForward layer are two key components of every Decoder layer. The generation task is memory bound because iterative decode and kv_cache require special management to reduce memory overheads. Intel® Extension for PyTorch* provides a lot of specific optimizations for these LLMs. 
 On the operator level, the extension provides highly efficient GEMM kernel to speed up Linear layer and customized operators to reduce the memory footprint. To better trade-off the performance and accuracy, different low-precision solutions e.g., smoothQuant is enabled. Besides, tensor parallel can also adopt to get lower latency for LLMs.
 
-These LLM-specific optimizations can be automatically applied with a single frontend API function in Python interface, `ipex.optimize_transformers()`. Check `optimize_transformers <./llm/llm_optimize_transformers.md>`_ for more details.
+These LLM-specific optimizations can be automatically applied with a single frontend API function in Python interface, `ipex.llm.optimize()`. Check `ipex.llm.optimize <./llm/llm_optimize_transformers.md>`_ for more details.
 
 .. toctree::
    :hidden:
@@ -13,7 +13,7 @@ These LLM-specific optimizations can be automatically applied with a single fron
 
    llm/llm_optimize_transformers
 
-Optimized Models List
+Validated Models List
 ---------------------
 
 LLM Inference
@@ -209,3 +209,4 @@ For more detailed information, check `WOQ INT4 <llm/int4_weight_only_quantizatio
    llm/int4_weight_only_quantization
 
 
+
diff --git a/docs/tutorials/llm/int4_weight_only_quantization.md b/docs/tutorials/llm/int4_weight_only_quantization.md
index 3f7c7c847..67b4c50b5 100644
--- a/docs/tutorials/llm/int4_weight_only_quantization.md
+++ b/docs/tutorials/llm/int4_weight_only_quantization.md
@@ -91,18 +91,18 @@ In Weight-Only Quantization INT4 case, when using `AutoModelForCausalLM.from_pre
                                                 scale_dtype=convert_dtype_str2torch("fp16"))
 ```
 
-When running on Intel® GPU, it will replace the linear in the model with `WeightOnlyQuantizedLinear`. After that, the model linear weight loaded by `ipex.optimize_transformers` is in INT4 format, and it contains not only weight and bias information, but also scales, zero_points, and blocksize information. When optimizing transformers at the front end, Intel® Extension for PyTorch\* will use `WeightOnlyQuantizedLinear` to initialize these information in the model if they are present, otherwise, it will use `IPEXTransformerLinear` to initialize the linear parameters in the model.
+When running on Intel® GPU, it will replace the linear in the model with `WeightOnlyQuantizedLinear`. After that, the model linear weight loaded by `ipex.llm.optimize` is in INT4 format, and it contains not only weight and bias information, but also scales, zero_points, and blocksize information. When optimizing transformers at the front end, Intel® Extension for PyTorch\* will use `WeightOnlyQuantizedLinear` to initialize these information in the model if they are present, otherwise, it will use `IPEXTransformerLinear` to initialize the linear parameters in the model.
 
 
 ### Weight-Only Quantization Runtime
-On Intel® GPU, after using `ipex.optimize_transformers`, Intel® Extension for PyTorch\* will automatically replace the original attention module with `IPEXTransformerAttnOptimizedInt4` and the original mlp module with `IPEXTransformerMLPOptimizedInt4` in the model.
+On Intel® GPU, after using `ipex.llm.optimize`, Intel® Extension for PyTorch\* will automatically replace the original attention module with `IPEXTransformerAttnOptimizedInt4` and the original mlp module with `IPEXTransformerMLPOptimizedInt4` in the model.
 
-The major changes between `IPEXTransformerAttnOptimizedInt4` for INT4 scenario and `ipex.optimize_transformers` for FP16 scenario include: replace the linear used to calculate qkv with `torch.ops.torch_ipex.mm_qkv_out_int4` and out_linear with `torch.ops.torch_ipex.mm_bias_int4`.
+The major changes between `IPEXTransformerAttnOptimizedInt4` for INT4 scenario and `ipex.llm.optimize` for FP16 scenario include: replace the linear used to calculate qkv with `torch.ops.torch_ipex.mm_qkv_out_int4` and out_linear with `torch.ops.torch_ipex.mm_bias_int4`.
 
-The major changes between `IPEXTransformerMLPOptimizedInt4` for INT4 scenario and `ipex.optimize_transformers` for FP16 scenario include: replace the linear used in mlp with `torch.ops.torch_ipex.mm_bias_int4`, if activation is used in the mlp module, then correspondingly, it will be replaced with our fused linear+activation kernel, such as `torch.ops.torch_ipex.mm_silu_mul_int4`.
+The major changes between `IPEXTransformerMLPOptimizedInt4` for INT4 scenario and `ipex.llm.optimize` for FP16 scenario include: replace the linear used in mlp with `torch.ops.torch_ipex.mm_bias_int4`, if activation is used in the mlp module, then correspondingly, it will be replaced with our fused linear+activation kernel, such as `torch.ops.torch_ipex.mm_silu_mul_int4`.
 
 ### Weight-Only Quantization Linear Dispatch
-As explained before, after applying `ipex.optimize_transformers`, The linear kernel that Intel® Extension for PyTorch* has registered to substitute the original linear will be used in the model.
+As explained before, after applying `ipex.llm.optimize`, The linear kernel that Intel® Extension for PyTorch* has registered to substitute the original linear will be used in the model.
 
 The method is: 
 
@@ -149,7 +149,7 @@ inputs = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
 qmodel = AutoModelForCausalLM.from_pretrained(model_name, load_in_4bit=True, device_map="xpu", trust_remote_code=True)
 
 # optimize the model with Intel® Extension for PyTorch*, it will improve performance.
-qmodel = ipex.optimize_transformers(qmodel, inplace=True, dtype=torch.float16, woq=True, device="xpu")
+qmodel = ipex.llm.optimize(qmodel, inplace=True, dtype=torch.float16, woq=True, device="xpu")
 
 output = qmodel.generate(inputs)
 ```
@@ -164,14 +164,14 @@ from intel_extension_for_transformers.transformers.modeling import AutoModelForC
 
 qmodel = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B", load_in_4bit=True, device_map="xpu", trust_remote_code=True)
 
-# Please note, saving model should be executed before ipex.optimize_transformers function is called. 
+# Please note, saving model should be executed before ipex.llm.optimize function is called. 
 model.save_pretrained("saved_dir")
 
 # Load model
 loaded_model = AutoModelForCausalLM.from_pretrained("saved_dir", trust_remote_code=True)
 
-# Before executed the loaded model, you can call ipex.optimize_transformers function.
-loaded_model = ipex.optimize_transformers(loaded_model, inplace=True, dtype=torch.float16, woq=True, device="xpu")
+# Before executed the loaded model, you can call ipex.llm.optimize function.
+loaded_model = ipex.llm.optimize(loaded_model, inplace=True, dtype=torch.float16, woq=True, device="xpu")
 
 output = loaded_model.generate(inputs)
 
@@ -184,5 +184,6 @@ bash run_benchmark_woq.sh
 ```
 
 >Note:
-> * Do save quantized model before call `optimize_transformers` function.
-> * The optimize_transformers function is designed to optimize transformer-based models within frontend python modules, with a particular focus on Large Language Models (LLMs). It provides optimizations for both model-wise and content-generation-wise. Please refer to [Transformers Optimization Frontend API](../../tutorials/llm/llm_optimize_transformers.md) for the detail of `optimize_transformers`.
+> * Do save quantized model before call `ipex.llm.optimize` function.
+> * The `ipex.llm.optimize` function is designed to optimize transformer-based models within frontend python modules, with a particular focus on Large Language Models (LLMs). It provides optimizations for both model-wise and content-generation-wise. Please refer to [Transformers Optimization Frontend API](../../tutorials/llm/llm_optimize_transformers.md) for the detail of `ipex.llm.optimize`.
+
diff --git a/docs/tutorials/llm/llm_optimize_transformers.md b/docs/tutorials/llm/llm_optimize_transformers.md
index 9d4437d0f..554b3be90 100644
--- a/docs/tutorials/llm/llm_optimize_transformers.md
+++ b/docs/tutorials/llm/llm_optimize_transformers.md
@@ -1,11 +1,11 @@
 Transformers Optimization Frontend API
 ======================================
 
-The new API function, `ipex.optimize_transformers`, is designed to optimize transformer-based models within frontend Python modules, with a particular focus on Large Language Models (LLMs). It provides optimizations for both model-wise and content-generation-wise. You just need to invoke the `ipex.optimize_transformers` function instead of the `ipex.optimize` function to apply all optimizations transparently.
+The new API function, `ipex.llm.optimize`, is designed to optimize transformer-based models within frontend Python modules, with a particular focus on Large Language Models (LLMs). It provides optimizations for both model-wise and content-generation-wise. You just need to invoke the `ipex.llm.optimize` function instead of the `ipex.optimize` function to apply all optimizations transparently.
 
 This API currently works for inference workloads. Support for training is undergoing. Currently, this API supports certain models. Supported model list can be found at [Overview](../llm.html#optimized-models).
 
-API documentation is available at [API Docs page](../api_doc.html#ipex.optimize_transformers).
+API documentation is available at [API Docs page](../api_doc.html#ipex.llm.optimize).
 
 ## Pseudocode of Common Usage Scenarios
 
@@ -23,7 +23,7 @@ device = "xpu"
 model= transformers.AutoModelForCausalLM(model_name_or_path).eval().to(device)
 
 amp_dtype = torch.float16 
-model = ipex.optimize_transformers(model.eval(), dtype=amp_dtype, device=device, inplace=True)
+model = ipex.llm.optimize(model.eval(), dtype=amp_dtype, device=device, inplace=True)
 
 # inference with model.generate()
 ...
@@ -122,3 +122,4 @@ Check Distributed Examples in [LLM example](https://github.com/intel/intel-exten
 
 
 
+
diff --git a/docs/tutorials/releases.md b/docs/tutorials/releases.md
index bbc9383ee..f2e0dbaee 100644
--- a/docs/tutorials/releases.md
+++ b/docs/tutorials/releases.md
@@ -1,6 +1,83 @@
 Releases
 =============
 
+## 2.3.110+xpu
+
+Intel® Extension for PyTorch\* v2.3.110+xpu is the new release which supports Intel® GPU platforms (Intel® Data Center GPU Flex Series, Intel® Data Center GPU Max Series and Intel® Arc™ A-Series Graphics) based on PyTorch\* 2.3.1.
+
+### Highlights
+
+- Intel® oneDNN v3.5.3 integration
+- Intel® oneAPI Base Toolkit 2024.2.1 compatibility
+- Large Language Model (LLM) optimization
+  
+  Intel® Extension for PyTorch\* provides a new dedicated module, `ipex.llm`, to host for Large Language Models (LLMs) specific APIs. With `ipex.llm`, Intel® Extension for PyTorch\* provides comprehensive LLM optimization on FP16 and INT4 datatypes. Specifically for low precision, Weight-Only Quantization is supported for various scenarios. And user can also run Intel® Extension for PyTorch\* with Tensor Parallel to fit in the multiple ranks or multiple nodes scenarios to get even better performance.
+
+  A typical API under this new module is `ipex.llm.optimize`, which is designed to optimize transformer-based models within frontend Python modules, with a particular focus on Large Language Models (LLMs). It provides optimizations for both model-wise and content-generation-wise. `ipex.llm.optimize` is an upgrade API to replace previous `ipex.optimize_transformers`, which will bring you more consistent LLM experience and performance. Below shows a simple example of `ipex.llm.optimize` for fp16 inference:
+
+  ```python
+    import torch
+    import intel_extension_for_pytorch as ipex
+    import transformers
+
+    model= transformers.AutoModelForCausalLM.from_pretrained(model_name_or_path).eval()
+
+    dtype = torch.float16
+    model = ipex.llm.optimize(model, dtype=dtype, device="xpu")
+
+    model.generate(YOUR_GENERATION_PARAMS)
+  ```
+    
+  More examples of this API can be found at [LLM optimization API](https://intel.github.io/intel-extension-for-pytorch/xpu/2.3.110+xpu/tutorials/api_doc.html#ipex.llm.optimize).
+
+  Besides that, we optimized more LLM inference models. A full list of optimized models can be found at [LLM Optimizations Overview](https://intel.github.io/intel-extension-for-pytorch/xpu/latest/tutorials/llm.html).
+
+- Serving framework support
+  
+  Typical LLM serving frameworks including [vLLM](https://github.com/vllm-project/vllm) and [TGI](https://huggingface.co/text-generation-inference) can co-work with Intel® Extension for PyTorch\* on Intel® GPU platforms (Intel® Data Center GPU Max 1550 and Intel® Arc™ A-Series Graphics). Besides the integration of LLM serving frameworks with `ipex.llm` module level APIs, we enhanced the performance and quality of underneath Intel® Extension for PyTorch\* operators such as paged attention and flash attention for better end to end model performance.
+
+- Prototype support of full fine-tuning and LoRA PEFT with mixed precision
+
+  Intel® Extension for PyTorch\* also provides new capability for supporting popular recipes with both full fine-tuning and [LoRA PEFT](https://github.com/huggingface/peft)  for mixed precision with BF16 and FP32. We optimized many typical LLM models including Llama 2 (7B and 70B), Llama 3 8B, Phi-3-Mini 3.8B model families and Chinese model Qwen-7B, on both single GPU and Multi-GPU (distributed fine-tuning based on PyTorch FSDP) use cases.
+
+### Known Issues
+
+Please refer to [Known Issues webpage](./known_issues.md).
+
+
+## 2.1.40+xpu
+
+Intel® Extension for PyTorch\* v2.1.40+xpu is a minor release which supports Intel® GPU platforms (Intel® Data Center GPU Flex Series, Intel® Data Center GPU Max Series，Intel® Arc™ A-Series Graphics and Intel® Core™ Ultra Processors with Intel® Arc™ Graphics) based on PyTorch\* 2.1.0.
+
+### Highlights
+
+- Intel® oneAPI Base Toolkit 2024.2.1 compatibility
+- Intel® oneDNN v3.5 integration
+- Intel® oneCCL 2021.13.1 integration
+- Intel® Core™ Ultra Processors with Intel® Arc™ Graphics (MTL-H) support on Windows (Prototype)
+- Bug fixing and other optimization
+  - Fix host memory leak [#4280](https://github.com/intel/intel-extension-for-pytorch/commit/5c252a1e34ccecc8e2e5d10ccc67f410ac7b87e2)
+  - Fix LayerNorm issue for undefined grad_input [#4317](https://github.com/intel/intel-extension-for-pytorch/commit/619cd9f5c300a876455411bcacc470bd94c923be)
+  - Replace FP64 device check method [#4354](https://github.com/intel/intel-extension-for-pytorch/commit/d60d45187b1dd891ec8aa2abc42eca8eda5cb242)
+  - Fix online doc search issue [#4358](https://github.com/intel/intel-extension-for-pytorch/commit/2e957315fdad776617e24a3222afa55f54b51507)
+  - Fix pdist unit test failure on client GPUs [#4361](https://github.com/intel/intel-extension-for-pytorch/commit/00f94497a94cf6d69ebba33ff95d8ab39113ecf4)
+  - Remove primitive cache from conv fwd [#4429](https://github.com/intel/intel-extension-for-pytorch/commit/bb1c6e92d4d11faac5b6fc01b226d27950b86579)
+  - Fix sdp bwd page fault with no grad bias [#4439](https://github.com/intel/intel-extension-for-pytorch/commit/d015f00011ad426af33bb970451331321417bcdb)  
+  - Fix implicit data conversion [#4463](https://github.com/intel/intel-extension-for-pytorch/commit/d6987649e58af0da4964175aed3286aef16c78c9)
+  - Fix compiler version parsing issue [#4468](https://github.com/intel/intel-extension-for-pytorch/commit/50b2b5933b6df6632a18d76bdec46b638750dc48)  
+  - Fix irfft invalid descriptor [#4480](https://github.com/intel/intel-extension-for-pytorch/commit/3e60e87cf011b643cc0e72d82c10b28417061d97)
+  - Change condition order to fix out-of-bound access in index [#4495](https://github.com/intel/intel-extension-for-pytorch/commit/8b74d6c5371ed0bd442279be42b0d454cb2b31b3)
+  - Add parameter check in embedding bag [#4504](https://github.com/intel/intel-extension-for-pytorch/commit/57174797bab9de2647abb8fdbcda638b0c694e01)
+  - Add the backward implementation for rms norm [#4527](https://github.com/intel/intel-extension-for-pytorch/commit/e4938e0a9cee15ffe2f8d205e0228c1842a5735c)
+  - Fix attn_mask for sdpa beam_search [#4557](https://github.com/intel/intel-extension-for-pytorch/commit/80ed47655b003fa132ac264b3d3008c298865473)
+  - Use data_ptr template instead of force data conversion [#4558](https://github.com/intel/intel-extension-for-pytorch/commit/eeb92d2f4c34f143fc76e409987543d42e68d065)
+  - Workaround windows AOT image size over 2GB issue on Intel® Core™ Ultra Processors with Intel® Arc™ Graphics [#4407](https://github.com/intel/intel-extension-for-pytorch/commit/d7ebba7c94374bdd12883ffd45d6670b96029d11) [#4450](https://github.com/intel/intel-extension-for-pytorch/commit/550fd767b723bd9a1a799b05be5d8ce073e6faf7)
+  
+### Known Issues
+
+Please refer to [Known Issues webpage](./known_issues.md).
+
+
 ## 2.1.30+xpu
 
 Intel® Extension for PyTorch\* v2.1.30+xpu is an update release which supports Intel® GPU platforms (Intel® Data Center GPU Flex Series, Intel® Data Center GPU Max Series and Intel® Arc™ A-Series Graphics) based on PyTorch\* 2.1.0.
diff --git a/docs/tutorials/technical_details.rst b/docs/tutorials/technical_details.rst
index 87e3476a3..292ae2384 100644
--- a/docs/tutorials/technical_details.rst
+++ b/docs/tutorials/technical_details.rst
@@ -17,7 +17,7 @@ Optimizers are a key part of the training workloads. Intel® Extension for PyTor
    technical_details/optimizer_fusion_gpu
  
 
-For more detailed information, check `Optimizer Fusion on CPU <technical_details/optimizer_fusion_cpu.md>`_, `Optimizer Fusion on GPU <technical_details/optimizer_fusion_gpu.md>`_ and `Split SGD <technical_details/split_sgd.html>`_.
+For more detailed information, check `Optimizer Fusion on GPU <technical_details/optimizer_fusion_gpu.md>`_.
 
 Ahead of Time Compilation (AOT) [GPU]
 -------------------------------------
diff --git a/examples/gpu/llm/README.md b/examples/gpu/llm/README.md
index 25564fa35..18f249fd3 100644
--- a/examples/gpu/llm/README.md
+++ b/examples/gpu/llm/README.md
@@ -54,6 +54,7 @@ cd examples/gpu/llm
 bash ./tools/env_setup.sh 7
 conda deactivate
 conda activate llm
+export OCL_ICD_VENDORS=/etc/OpenCL/vendors
 source ./tools/env_activate.sh [inference|fine-tuning]
 ```
 
@@ -105,6 +106,7 @@ bash ./tools/env_setup.sh 3 <DPCPP_ROOT> <ONEMKL_ROOT> <ONECCL_ROOT> <MPI_ROOT>
 
 conda deactivate
 conda activate llm
+export OCL_ICD_VENDORS=/etc/OpenCL/vendors
 source ./tools/env_activate.sh [inference|fine-tuning]
 ```
 
diff --git a/examples/gpu/llm/fine-tuning/Llama2/README.md b/examples/gpu/llm/fine-tuning/Llama2/README.md
index ccff39fba..66dcf05bb 100644
--- a/examples/gpu/llm/fine-tuning/Llama2/README.md
+++ b/examples/gpu/llm/fine-tuning/Llama2/README.md
@@ -16,7 +16,7 @@ huggingface-cli login --token <your_token_here>
 For Alpaca dataset, you can get here: [Alpaca dataset](https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json).
 ```
 git clone https://github.com/tatsu-lab/stanford_alpaca
-cd standford_alpaca
+cd stanford_alpaca
 mv alpaca_data.json <Llama2_folder>
 ```
 
diff --git a/examples/gpu/llm/fine-tuning/Phi3/README.md b/examples/gpu/llm/fine-tuning/Phi3/README.md
index e1e74f349..7bdc8fde7 100644
--- a/examples/gpu/llm/fine-tuning/Phi3/README.md
+++ b/examples/gpu/llm/fine-tuning/Phi3/README.md
@@ -68,7 +68,7 @@ Example: Phi-3 Mini 4k LoRA fine-tuning on single card. The default dataset `fin
 ```bash
 export TORCH_LLM_ALLREDUCE=1
 
-export model="meta-llama/Meta-Llama-3-8B"
+export model="microsoft/Phi-3-mini-4k-instruct"
 
 python phi3_ft.py \
     --model_name_or_path ${model} \
diff --git a/examples/gpu/llm/fine-tuning/README.md b/examples/gpu/llm/fine-tuning/README.md
index cd5dd2af4..af4f6c619 100644
--- a/examples/gpu/llm/fine-tuning/README.md
+++ b/examples/gpu/llm/fine-tuning/README.md
@@ -16,7 +16,7 @@ Our examples integrate with the popular tools and libraries from the ecosystem:
 - [Accelerate](https://github.com/huggingface/accelerate) for Multi-GPUs launch
 
 
-### LLM fine-tuning optimized with Intel® Data Center Max 1550 GPU on Linux
+### LLM fine-tuning validated with Intel® Data Center Max 1550 GPU on Linux
 
 **Note**:
 Here we mainly focus on the memory-constrained fine-tuning on single GPU, and provide examples for LoRA fine-tuning. If you want to take a try for full fine-tuning, you could set the number of GPU in distributed cases as 1, and make sure your GPU memory is large enough for model states (parameters, gradients, optimizer states) and residual states (activation, temporary buffers and unusable fragmented memory).
@@ -38,7 +38,7 @@ Here we mainly focus on the memory-constrained fine-tuning on single GPU, and pr
 
 \* Intel® Data Center Max 1550 GPU: support all the models in the model list above.
 
-### LLM fine-tuning optimized with Intel® Core™ Ultra Processors with Intel® Arc™ Graphics 
+### LLM fine-tuning validated with Intel® Core™ Ultra Processors with Intel® Arc™ Graphics 
 
 | MODEL FAMILY | Verified < MODEL ID > (Hugging Face hub)| Mixed Precision (BF16+FP32) | Full fine-tuning  | LoRA |  
 |---|:---:|:---:|:---:|:---:|
diff --git a/examples/gpu/llm/fine-tuning/requirements.txt b/examples/gpu/llm/fine-tuning/requirements.txt
index c18381f97..33493a997 100644
--- a/examples/gpu/llm/fine-tuning/requirements.txt
+++ b/examples/gpu/llm/fine-tuning/requirements.txt
@@ -5,3 +5,4 @@ transformers==4.41.2
 tokenizers>=0.13.3
 wandb==0.17.5
 trl==0.9.4
+accelerate==0.28.0
diff --git a/examples/gpu/llm/inference/README.md b/examples/gpu/llm/inference/README.md
index 71a1e35ad..0ca769e9e 100644
--- a/examples/gpu/llm/inference/README.md
+++ b/examples/gpu/llm/inference/README.md
@@ -7,7 +7,7 @@ Here you can find the inference benchmarking scripts for large language models (
 - Cover model generation inference with low precision cases for different models with best performance and accuracy (fp16 AMP and weight only quantization)
 
 
-## Optimized Models
+## Validated Models
 
 Currently, only support Transformers 4.38.1. Support for newer versions of Transformers and more models will be available in the future.
 
diff --git a/examples/gpu/llm/inference/requirements.txt b/examples/gpu/llm/inference/requirements.txt
index a63d19ddc..07376214b 100644
--- a/examples/gpu/llm/inference/requirements.txt
+++ b/examples/gpu/llm/inference/requirements.txt
@@ -1,2 +1,3 @@
 numpy
 transformers==4.38.1
+deepspeed==0.14.4
diff --git a/examples/gpu/llm/requirements.txt b/examples/gpu/llm/requirements.txt
index 2d6a87af2..9e7cd889e 100644
--- a/examples/gpu/llm/requirements.txt
+++ b/examples/gpu/llm/requirements.txt
@@ -13,4 +13,3 @@ py-cpuinfo
 bitsandbytes
 transformers_stream_generator
 einops
-deepspeed==0.14.4
diff --git a/scripts/compile_bundle.bat b/scripts/compile_bundle.bat
index 04231c9cb..ef97cdb2c 100644
--- a/scripts/compile_bundle.bat
+++ b/scripts/compile_bundle.bat
@@ -1,7 +1,7 @@
 @echo off
 setlocal
 
-set "VER_IPEX=xpu-main"
+set "VER_IPEX=v2.3.110+xpu"
 set "ENABLE_ONEAPI_INTEGRATION=1"
 
 if "%~2"=="" (
diff --git a/scripts/tools/compilation_helper/basekit_driver_install_helper.sh b/scripts/tools/compilation_helper/basekit_driver_install_helper.sh
index 2a384fcd2..126da9d50 100644
--- a/scripts/tools/compilation_helper/basekit_driver_install_helper.sh
+++ b/scripts/tools/compilation_helper/basekit_driver_install_helper.sh
@@ -160,7 +160,7 @@ function install-driver() {
         ${SUDO} dnf install -y intel-opencl-24.26.30049.10 \
         level-zero-1.17.6 \
         level-zero-devel-1.17.6 \
-        intel-level-zero-gpu-1.3.30049 \
+        intel-level-zero-gpu-1.3.30049.10 \
         intel-ocloc-24.26.30049.10 \
         xpu-smi-1.2.35
     fi
@@ -168,7 +168,7 @@ function install-driver() {
         ${SUDO} zypper install -y --oldpackage intel-opencl-24.26.30049.10 \
         level-zero-1.17.6 \
         level-zero-devel-1.17.6 \
-        intel-level-zero-gpu-1.3.30049 \
+        intel-level-zero-gpu-1.3.30049.10 \
         intel-ocloc-24.26.30049.10 \
         xpu-smi-1.2.35
     fi