llama.cpp for SYCL

• Background
• Recommended Release
• News
• OS
• Hardware
• Docker
• Linux
• Windows
• Environment Variable
• Design Rule
• Known Issue
• Q&A
• TODO

Background

SYCL is a high-level parallel programming model designed to improve developers productivity writing code across various hardware accelerators such as CPUs, GPUs, and FPGAs. It is a single-source language designed for heterogeneous computing and based on standard C++17.

oneAPI is an open ecosystem and a standard-based specification, supporting multiple architectures including but not limited to Intel CPUs, GPUs and FPGAs. The key components of the oneAPI ecosystem include:

• DPCPP (Data Parallel C++): The primary oneAPI SYCL implementation, which includes the icpx/icx Compilers.
• oneAPI Libraries: A set of highly optimized libraries targeting multiple domains (e.g. Intel oneMKL, oneMath and oneDNN).
• oneAPI LevelZero: A high performance low level interface for fine-grained control over Intel iGPUs and dGPUs.

Llama.cpp + SYCL

The llama.cpp SYCL backend is primarily designed for Intel GPUs. SYCL cross-platform capabilities enable support for other vendor GPUs as well.

Recommended Release

The following releases are verified and recommended:

News

• 2026.03

  • Support Flash-Attention: less memory usage, performance impact depends on LLM.

• 2026.02

  • Remove support for Nvidia & AMD GPU, because the oneAPI plugin for Nvidia & AMD GPU is unavailable: download/installation channels are out of work. User can't build up the software for Nvidia & AMD GPU.

• 2025.11

  • Support malloc memory on device more than 4GB.

• 2025.2

  • Optimize MUL_MAT Q4_0 on Intel GPU for all dGPUs and built-in GPUs since MTL. Increase the performance of LLM (llama-2-7b.Q4_0.gguf) 21%-87% on Intel GPUs (MTL, ARL-H, Arc, Flex, PVC).

    GPU	Base tokens/s	Increased tokens/s	Percent
    PVC 1550	39	73	+87%
    Flex 170	39	50	+28%
    Arc A770	42	55	+30%
    MTL	13	16	+23%
    ARL-H	14	17	+21%

• 2024.11

  • Use syclcompat to improve the performance on some platforms. This requires to use oneAPI 2025.0 or newer.

• 2024.8

  • Use oneDNN as the default GEMM library, improve the compatibility for new Intel GPUs.

• 2024.5

  • Performance is increased: 34 -> 37 tokens/s of llama-2-7b.Q4_0 on Arc A770.
  • Arch Linux is verified successfully.

• 2024.4

  • Support data types: GGML_TYPE_IQ4_NL, GGML_TYPE_IQ4_XS, GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ3_S, GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M.

• 2024.3

  • Release binary files of Windows.
  • A blog is published: Run LLM on all Intel GPUs Using llama.cpp: intel.com or medium.com.
  • New base line is ready: tag b2437.
  • Support multiple cards: --split-mode: [none|layer]; not support [row], it's on developing.
  • Support to assign main GPU by --main-gpu, replace $GGML_SYCL_DEVICE.
  • Support detecting all GPUs with level-zero and same top Max compute units.
  • Support OPs
    • hardsigmoid
    • hardswish
    • pool2d

• 2024.1

  • Create SYCL backend for Intel GPU.
  • Support Windows build

OS

Hardware

Intel GPU

SYCL backend supports Intel GPU Family:

• Intel Data Center Max Series
• Intel Flex Series, Arc Series
• Intel Built-in Arc GPU
• Intel iGPU in Core CPU (11th Generation Core CPU and newer, refer to oneAPI supported GPU).

On older Intel GPUs, you may try OpenCL although the performance is not optimal, and some GPUs may not support OpenCL nor have any GPGPU capabilities.

Verified devices

Notes:

• Memory

  • The device memory is a limitation when running a large model. The loaded model size, llm_load_tensors: buffer_size, is displayed in the log when running ./bin/llama-completion.
  • Please make sure the GPU shared memory from the host is large enough to account for the model's size. For e.g. the llama-2-7b.Q4_0 requires at least 8.0GB for integrated GPU and 4.0GB for discrete GPU.

• Execution Unit (EU)

  • If the iGPU has less than 80 EUs, the inference speed will likely be too slow for practical use.

Other Vendor GPU

NA

Docker

The docker build option is currently limited to Intel GPU targets.

Build image

# Using FP32
docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=OFF" --target light -f .devops/intel.Dockerfile .

# Using FP16
docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" --target light -f .devops/intel.Dockerfile .

Notes:

You can also use the .devops/llama-server-intel.Dockerfile, which builds the "server" alternative. Check the documentation for Docker to see the available images.

Run container

# First, find all the DRI cards
ls -la /dev/dri
# Then, pick the card that you want to use (here for e.g. /dev/dri/card1).
docker run -it --rm -v "/path/to/models:/models" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card0:/dev/dri/card0 llama-cpp-sycl -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -c 4096 -s 0

Notes:

• Docker has been tested successfully on native Linux. WSL support has not been verified yet.
• You may need to install Intel GPU driver on the host machine (Please refer to the Linux configuration for details).

Linux

I. Setup Environment

1. Install GPU drivers

• Intel GPU

Intel data center GPUs drivers installation guide and download page can be found here: Get intel dGPU Drivers.

Note: for client GPUs (iGPU & Arc A-Series), please refer to the client iGPU driver installation.

Once installed, add the user(s) to the video and render groups.

sudo usermod -aG render $USER
sudo usermod -aG video $USER

Note: logout/re-login for the changes to take effect.

Verify installation through clinfo:

sudo apt install clinfo
sudo clinfo -l

Sample output:

Platform #0: Intel(R) OpenCL Graphics
 `-- Device #0: Intel(R) Arc(TM) A770 Graphics

Platform #0: Intel(R) OpenCL HD Graphics
 `-- Device #0: Intel(R) Iris(R) Xe Graphics [0x9a49]

2. Install Intel® oneAPI Base toolkit

SYCL backend depends on:

• Intel® oneAPI DPC++/C++ compiler/running-time.

• Intel® oneAPI DPC++/C++ library (oneDPL).

• Intel® oneAPI Deep Neural Network Library (oneDNN).

• Intel® oneAPI Math Kernel Library (oneMKL).

• For Intel GPU

All above are included in both Intel® oneAPI Base toolkit and Intel® Deep Learning Essentials packages.

It's recommended to install Intel® Deep Learning Essentials which only provides the necessary libraries with less size.

The Intel® oneAPI Base toolkit and Intel® Deep Learning Essentials can be obtained from the official Intel® oneAPI Base Toolkit page.

Please follow the instructions for downloading and installing the Toolkit for Linux, and preferably keep the default installation values unchanged, notably the installation path (/opt/intel/oneapi by default).

Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable.

Upon a successful installation, SYCL is enabled for the available intel devices, along with relevant libraries such as oneAPI oneDNN for Intel GPUs.

3. Verify installation and environment

In order to check the available SYCL devices on the machine, please use the sycl-ls command.

source /opt/intel/oneapi/setvars.sh
sycl-ls

• Intel GPU

When targeting an intel GPU, the user should expect one or more devices among the available SYCL devices. Please make sure that at least one GPU is present via sycl-ls, for instance [level_zero:gpu] in the sample output below:

[level_zero:gpu][level_zero:0] Intel(R) oneAPI Unified Runtime over Level-Zero, Intel(R) Arc(TM) A770 Graphics 12.55.8 [1.3.29735+27]
[level_zero:gpu][level_zero:1] Intel(R) oneAPI Unified Runtime over Level-Zero, Intel(R) UHD Graphics 730 12.2.0 [1.3.29735+27]
[opencl:cpu][opencl:0] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i5-13400 OpenCL 3.0 (Build 0) [2025.20.8.0.06_160000]
[opencl:gpu][opencl:1] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics OpenCL 3.0 NEO  [24.39.31294]
[opencl:gpu][opencl:2] Intel(R) OpenCL Graphics, Intel(R) UHD Graphics 730 OpenCL 3.0 NEO  [24.39.31294]

II. Build llama.cpp

Intel GPU

./examples/sycl/build.sh

or

# Export relevant ENV variables
source /opt/intel/oneapi/setvars.sh

# Option 1: Use FP32 (recommended for better performance in most cases)
cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx

# Option 2: Use FP16
cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON

# build all binary
cmake --build build --config Release -j -v

It is possible to come across some precision issues when running tests that stem from using faster instructions, which can be circumvented by setting the environment variable SYCL_PROGRAM_COMPILE_OPTIONS as -cl-fp32-correctly-rounded-divide-sqrt

III. Run the inference

Retrieve and prepare model

You can refer to the general Obtaining and quantizing models guide for model preparation, or download an already quantized model like llama-2-7b.Q4_0.gguf or Meta-Llama-3-8B-Instruct-Q4_0.gguf.

Check device

1. Enable oneAPI running environment

source /opt/intel/oneapi/setvars.sh

2. List devices information

Similar to the native sycl-ls, available SYCL devices can be queried as follow:

./build/bin/llama-ls-sycl-device

This command will only display the selected backend that is supported by SYCL. The default backend is level_zero. For example, in a system with 2 intel GPU it would look like the following:

found 2 SYCL devices:

|  |                  |                                             |Compute   |Max compute|Max work|Max sub|               |
|ID|       Device Type|                                         Name|capability|units      |group   |group  |Global mem size|
|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|
| 0|[level_zero:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       1.3|        512|    1024|     32|    16225243136|
| 1|[level_zero:gpu:1]|                    Intel(R) UHD Graphics 770|       1.3|         32|     512|     32|    53651849216|

Choose level-zero devices

Execute

Choose one of following methods to run.

1. Script

• Use device 0:

./examples/sycl/test.sh -mg 0

• Use multiple devices:

./examples/sycl/test.sh

2. Command line Launch inference

There are two device selection modes:

• Single device: Use one device assigned by user. Default device id is 0.
• Multiple devices: Automatically choose the devices with the same backend.

In two device selection modes, the default SYCL backend is level_zero, you can choose other backend supported by SYCL by setting environment variable ONEAPI_DEVICE_SELECTOR.

Examples:

• Use device 0:

ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm none -mg 0 --mmap

• Use multiple devices:

ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm layer --mmap

Notes:

• Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow:

detect 1 SYCL GPUs: [0] with top Max compute units:512

Or

use 1 SYCL GPUs: [0] with Max compute units:512

Windows

Install GPU driver

Intel GPU drivers instructions guide and download page can be found here: Get Intel GPU Drivers.

Option 1: download the binary package directly

Download the binary package for Windows from: https://github.com/ggml-org/llama.cpp/releases.

Extract the package to local folder, run the llama tools directly. Refer to Run the inference.

Note, the package includes the SYCL running time and all depended dll files, no need to install oneAPI package and activte them.

Option 2: build locally from the source code.

I. Setup environment

1. Install Visual Studio

If you already have a recent version of Microsoft Visual Studio, you can skip this step. Otherwise, please refer to the official download page for Microsoft Visual Studio.

2. Install Intel® oneAPI Base toolkit

SYCL backend depends on:

• Intel® oneAPI DPC++/C++ compiler/running-time.
• Intel® oneAPI DPC++/C++ library (oneDPL).
• Intel® oneAPI Deep Neural Network Library (oneDNN).
• Intel® oneAPI Math Kernel Library (oneMKL).

All above are included in both Intel® oneAPI Base toolkit and Intel® Deep Learning Essentials packages.

It's recommended to install Intel® Deep Learning Essentials which only provides the necessary libraries with less size.

The Intel® oneAPI Base toolkit and Intel® Deep Learning Essentials can be obtained from the official Intel® oneAPI Base Toolkit page.

Please follow the instructions for downloading and installing the Toolkit for Windows, and preferably keep the default installation values unchanged, notably the installation path (C:\Program Files (x86)\Intel\oneAPI by default).

Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable.

b. Enable oneAPI running environment:

• Type "oneAPI" in the search bar, then open the Intel oneAPI command prompt for Intel 64 for Visual Studio 2022 App.

• On the command prompt, enable the runtime environment with the following:

"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64

• if you are using Powershell, enable the runtime environment with the following:

cmd.exe "/K" '"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" && powershell'

c. Verify installation

In the oneAPI command line, run the following to print the available SYCL devices:

sycl-ls.exe

There should be one or more level-zero GPU devices displayed as [ext_oneapi_level_zero:gpu]. Below is example of such output detecting an intel Iris Xe GPU as a Level-zero SYCL device:

Output (example):

[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2  [2023.16.10.0.17_160000]
[opencl:cpu:1] Intel(R) OpenCL, 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000]
[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Iris(R) Xe Graphics OpenCL 3.0 NEO  [31.0.101.5186]
[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044]

3. Install build tools

a. Download & install cmake for Windows: https://cmake.org/download/ (CMake can also be installed from Visual Studio Installer) b. The new Visual Studio will install Ninja as default. (If not, please install it manually: https://ninja-build.org/)

II. Build llama.cpp

You could download the release package for Windows directly, which including binary files and depended oneAPI dll files.

Choose one of following methods to build from source code.

Option 1: Script

.\examples\sycl\win-build-sycl.bat

Option 2: CMake

On the oneAPI command line window, step into the llama.cpp main directory and run the following:

@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force

# Option 1: Use FP32 (recommended for better performance in most cases)
cmake -B build -G "Ninja" -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release

# Option 2: Or FP16
cmake -B build -G "Ninja" -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release -DGGML_SYCL_F16=ON

cmake --build build --config Release -j

Or, use CMake presets to build:

cmake --preset x64-windows-sycl-release
cmake --build build-x64-windows-sycl-release -j --target llama-completion

cmake -DGGML_SYCL_F16=ON --preset x64-windows-sycl-release
cmake --build build-x64-windows-sycl-release -j --target llama-completion

cmake --preset x64-windows-sycl-debug
cmake --build build-x64-windows-sycl-debug -j --target llama-completion

Option 3: Visual Studio

You have two options to use Visual Studio to build llama.cpp:

• As CMake Project using CMake presets.
• Creating a Visual Studio solution to handle the project.

Note:

All following commands are executed in PowerShell.

- Open as a CMake Project

You can use Visual Studio to open the llama.cpp folder directly as a CMake project. Before compiling, select one of the SYCL CMake presets:

• x64-windows-sycl-release

• x64-windows-sycl-debug

Notes:

• For a minimal experimental setup, you can build only the inference executable using:

  PowershellCopy

  cmake --build build --config Release -j --target llama-completion
  

- Generating a Visual Studio Solution

You can use Visual Studio solution to build and work on llama.cpp on Windows. You need to convert the CMake Project into a .sln file.

If you want to use the Intel C++ Compiler for the entire llama.cpp project, run the following command:

cmake -B build -G "Visual Studio 17 2022" -T "Intel C++ Compiler 2025" -A x64 -DGGML_SYCL=ON -DCMAKE_BUILD_TYPE=Release

If you prefer to use the Intel C++ Compiler only for ggml-sycl, ensure that ggml and its backend libraries are built as shared libraries ( i.e. -DBUILD_SHARED_LIBRARIES=ON, this is default behaviour):

cmake -B build -G "Visual Studio 17 2022" -A x64 -DGGML_SYCL=ON -DCMAKE_BUILD_TYPE=Release \
      -DSYCL_INCLUDE_DIR="C:\Program Files (x86)\Intel\oneAPI\compiler\latest\include" \
      -DSYCL_LIBRARY_DIR="C:\Program Files (x86)\Intel\oneAPI\compiler\latest\lib"

If successful the build files have been written to: path/to/llama.cpp/build Open the project file build/llama.cpp.sln with Visual Studio.

Once the Visual Studio solution is created, follow these steps:

1. Open the solution in Visual Studio.

2. Right-click on ggml-sycl and select Properties.

3. In the left column, expand C/C++ and select DPC++.

4. In the right panel, find Enable SYCL Offload and set it to Yes.

5. Apply the changes and save.

Navigation Path:

Properties -> C/C++ -> DPC++ -> Enable SYCL Offload (Yes)

Now, you can build llama.cpp with the SYCL backend as a Visual Studio project. To do it from menu: Build -> Build Solution. Once it is completed, final results will be in build/Release/bin

Additional Note

• You can avoid specifying SYCL_INCLUDE_DIR and SYCL_LIBRARY_DIR in the CMake command by setting the environment variables:

  • SYCL_INCLUDE_DIR_HINT

  • SYCL_LIBRARY_DIR_HINT

• Above instruction has been tested with Visual Studio 17 Community edition and oneAPI 2025.0. We expect them to work also with future version if the instructions are adapted accordingly.

III. Run the inference

Retrieve and prepare model

You can refer to the general Obtaining and quantizing models guide for model preparation, or download an already quantized model like llama-2-7b.Q4_0.gguf or Meta-Llama-3-8B-Instruct-Q4_0.gguf.

Check device

1. Enable oneAPI running environment

On the oneAPI command line window, run the following and step into the llama.cpp directory:

"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64

2. List devices information

Similar to the native sycl-ls, available SYCL devices can be queried as follow:

build\bin\llama-ls-sycl-device.exe

This command will only display the selected backend that is supported by SYCL. The default backend is level_zero. For example, in a system with 2 Intel GPU it would look like the following:

found 2 SYCL devices:
|  |                  |                                             |Compute   |Max compute|Max work|Max sub|               |
|ID|       Device Type|                                         Name|capability|units      |group   |group  |Global mem size|
|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|
| 0|[level_zero:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       1.3|        512|    1024|     32|    16225243136|
| 1|[level_zero:gpu:1]|                    Intel(R) UHD Graphics 770|       1.3|         32|     512|     32|    53651849216|

Choose level-zero devices

Execute

Choose one of following methods to run.

1. Script

examples\sycl\win-test.bat

2. Command line

Launch inference

There are two device selection modes:

• Single device: Use one device assigned by user. Default device id is 0.
• Multiple devices: Automatically choose the devices with the same backend.

In two device selection modes, the default SYCL backend is level_zero, you can choose other backend supported by SYCL by setting environment variable ONEAPI_DEVICE_SELECTOR.

Examples:

• Use device 0:

build\bin\llama-completion.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm none -mg 0 --mmap

• Use multiple devices:

build\bin\llama-completion.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm layer --mmap

Note:

• Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow:

detect 1 SYCL GPUs: [0] with top Max compute units:512

Or

use 1 SYCL GPUs: [0] with Max compute units:512

Environment Variable

Build

1. FP32 or FP16 have different performance impact to LLM. Recommended to test them for better prompt processing performance on your models. You need to rebuild the code after change GGML_SYCL_F16=OFF/ON.

Runtime

Design Rule

• Open to all contributors.

• All code change should be useful to user:

  • Fix bug.
  • Add new function.
  • Improve the performance/usage.
  • Make code be easy to maintain.
  • ...

• Don't accept the codes of following cases:

  • Break legacy function.
  • Reduce the performance of legacy case in default.
  • Not completed work/the functionality cannot be demonstrated.

• Encourage to use environment variable to control features to be opened/closed.

  • User can evaluate the feature without rebuild the code.
  • Recommend the best features to user by setting them be opened as default.

• Design the code based on the published official releases of oneAPI packages: compiler, library, driver, OS kernel.

• Developers need to maintain the code they submit.

Known Issues

• Split-mode:[row] is not supported.

• Missed the AOT (Ahead-of-Time) in buiding.

  • Good: build quickly, smaller size of binary file.
  • Bad: The startup is slow (JIT) in first time, but subsequent performance is unaffected.

Q&A

• Error: error while loading shared libraries: libsycl.so: cannot open shared object file: No such file or directory.

  • Potential cause: Unavailable oneAPI installation or not set ENV variables.
  • Solution: Install oneAPI base toolkit and enable its ENV through: source /opt/intel/oneapi/setvars.sh.

• General compiler error:

  • Remove build folder or try a clean-build.

• I can not see [ext_oneapi_level_zero:gpu] after installing the GPU driver on Linux.

  Please double-check with sudo sycl-ls.

  If it's present in the list, please add video/render group to your user then logout/login or restart your system:

  Copy

  sudo usermod -aG render $USER
  sudo usermod -aG video $USER
  

  Otherwise, please double-check the GPU driver installation steps.

• Can I report Ollama issue on Intel GPU to llama.cpp SYCL backend?

  No. We can't support Ollama issue directly, because we aren't familiar with Ollama.

  Suggest reproducing on llama.cpp and report similar issue to llama.cpp. We will support it.

  It's same for other projects including llama.cpp SYCL backend.

• Native API failed. Native API returns: 39 (UR_RESULT_ERROR_OUT_OF_DEVICE_MEMORY), ggml_backend_sycl_buffer_type_alloc_buffer: can't allocate 3503030272 Bytes of memory on device, or failed to allocate SYCL0 buffer

  You are running out of Device Memory.

  Reason	Solution
  The default context is too big. It leads to excessive memory usage.	Set -c 8192 or a smaller value.
  The model is too big and requires more memory than what is available.	Choose a smaller model or change to a smaller quantization, like Q5 -> Q4;

Alternatively, use more than one device to load model.|

• ggml_backend_sycl_buffer_type_alloc_buffer: can't allocate 5000000000 Bytes of memory on device

  You need to enable to support 4GB memory malloc by:

  Copy

    export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
    set UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
  

GitHub contribution:

Please add the [SYCL] prefix/tag in issues/PRs titles to help the SYCL contributors to check/address them without delay.

TODO

• Review ZES_ENABLE_SYSMAN: https://github.com/intel/compute-runtime/blob/master/programmers-guide/SYSMAN.md#support-and-limitations