llama.cpp for SYCL

Background
Recommended Release
News
OS
Hardware
Docker
Linux
Windows
Environment Variable
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.
Nvidia & AMD Plugins: These are plugins extending oneAPI's DPCPP support to SYCL on Nvidia and AMD GPU targets.

Llama.cpp + SYCL

The llama.cpp SYCL backend is primarily designed for Intel GPUs. SYCL cross-platform capabilities enable support for Nvidia GPUs as well, with limited support for AMD.

Recommended Release

The following releases are verified and recommended:

Commit ID	Tag	Release	Verified Platform	Update date
24e86cae7219b0f3ede1d5abdf5bf3ad515cccb8	b5377	llama-b5377-bin-win-sycl-x64.zip	ArcB580/Linux/oneAPI 2025.1 LNL Arc GPU/Windows 11/oneAPI 2025.1.1	2025-05-15
3bcd40b3c593d14261fb2abfabad3c0fb5b9e318	b4040	llama-b4040-bin-win-sycl-x64.zip	Arc770/Linux/oneAPI 2024.1 MTL Arc GPU/Windows 11/oneAPI 2024.1	2024-11-19
fb76ec31a9914b7761c1727303ab30380fd4f05c	b3038	llama-b3038-bin-win-sycl-x64.zip	Arc770/Linux/oneAPI 2024.1 MTL Arc GPU/Windows 11/oneAPI 2024.1

News

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%
  
  Arc770 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 Arc770.
- 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

OS	Status	Verified
Linux	Support	Ubuntu 22.04, Fedora Silverblue 39, Arch Linux
Windows	Support	Windows 11

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).

Verified devices

Intel GPU	Status	Verified Model
Intel Data Center Max Series	Support	Max 1550, 1100
Intel Data Center Flex Series	Support	Flex 170
Intel Arc Series	Support	Arc 770, 730M, Arc A750, B580
Intel built-in Arc GPU	Support	built-in Arc GPU in Meteor Lake, Arrow Lake, Lunar Lake
Intel iGPU	Support	iGPU in 13700k, 13400, i5-1250P, i7-1260P, i7-1165G7

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-cli.
- 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

Verified devices

Nvidia GPU	Status	Verified Model
Ampere Series	Supported	A100, A4000
Ampere Series (Mobile)	Supported	RTX 40 Series

AMD GPU	Status	Verified Model
Radeon Pro	Experimental	W6800
Radeon RX	Experimental	6700 XT

Note: AMD GPU support is highly experimental and is incompatible with F16. Additionally, it only supports GPUs with a sub_group_size (warp size) of 32.

Docker

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

Build image

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

# Using FP32
docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=OFF" --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

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]

Nvidia GPU

In order to target Nvidia GPUs through SYCL, please make sure the CUDA/CUBLAS native requirements -found here- are installed.

AMD GPU

To target AMD GPUs with SYCL, the ROCm stack must be installed first.

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.

Verified release
2025.2.1
2025.1
2024.1

Adding support to Nvidia GPUs

oneAPI Plugin: In order to enable SYCL support on Nvidia GPUs, please install the Codeplay oneAPI Plugin for Nvidia GPUs. User should also make sure the plugin version matches the installed base toolkit one (previous step) for a seamless "oneAPI on Nvidia GPU" setup.

oneDNN: The current oneDNN releases (shipped with the oneAPI base-toolkit) do not include the NVIDIA backend. Therefore, oneDNN must be compiled from source to enable the NVIDIA target:

git clone https://github.com/oneapi-src/oneDNN.git
cd oneDNN
cmake -GNinja -Bbuild-nvidia -DDNNL_CPU_RUNTIME=DPCPP -DDNNL_GPU_RUNTIME=DPCPP -DDNNL_GPU_VENDOR=NVIDIA -DONEDNN_BUILD_GRAPH=OFF -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
cmake --build build-nvidia --config Release

Adding support to AMD GPUs

oneAPI Plugin: In order to enable SYCL support on AMD GPUs, please install the Codeplay oneAPI Plugin for AMD GPUs. As with Nvidia GPUs, the user should also make sure the plugin version matches the installed base toolkit.

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]

Nvidia GPU

Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA device [cuda:gpu] as below:

[opencl:acc][opencl:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2  [2023.16.12.0.12_195853.xmain-hotfix]
[opencl:cpu][opencl:1] Intel(R) OpenCL, Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz OpenCL 3.0 (Build 0) [2023.16.12.0.12_195853.xmain-hotfix]
[cuda:gpu][cuda:0] NVIDIA CUDA BACKEND, NVIDIA A100-PCIE-40GB 8.0 [CUDA 12.5]

AMD GPU

For AMD GPUs we should expect at least one SYCL-HIP device [hip:gpu]:

[opencl:cpu][opencl:0] Intel(R) OpenCL, 12th Gen Intel(R) Core(TM) i9-12900K OpenCL 3.0 (Build 0) [2024.18.6.0.02_160000]
[hip:gpu][hip:0] AMD HIP BACKEND, AMD Radeon PRO W6800 gfx1030 [HIP 60140.9]

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

Nvidia GPU

The SYCL backend depends on oneMath for Nvidia and AMD devices. By default it is automatically built along with the project. A specific build can be provided by setting the CMake flag -DoneMath_DIR=/path/to/oneMath/install/lib/cmake/oneMath.

# Build LLAMA with Nvidia BLAS acceleration through SYCL
# Setting GGML_SYCL_DEVICE_ARCH is optional but can improve performance
GGML_SYCL_DEVICE_ARCH=sm_80 # Example architecture

# Option 1: Use FP32 (recommended for better performance in most cases)
cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DDNNL_DIR=/path/to/oneDNN/build-nvidia/install/lib/cmake/dnnl

# Option 2: Use FP16
cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON -DDNNL_DIR=/path/to/oneDNN/build-nvidia/install/lib/cmake/dnnl

# 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 passing the -fno-fast-math flag to the compiler.

AMD GPU

The SYCL backend depends on oneMath for Nvidia and AMD devices. By default it is automatically built along with the project. A specific build can be provided by setting the CMake flag -DoneMath_DIR=/path/to/oneMath/install/lib/cmake/oneMath.

# Build LLAMA with rocBLAS acceleration through SYCL

## AMD
# Use FP32, FP16 is not supported
# Find your GGML_SYCL_DEVICE_ARCH with rocminfo, under the key 'Name:'
GGML_SYCL_DEVICE_ARCH=gfx90a # Example architecture
cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=AMD -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx

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

III. Run the inference

Retrieve and prepare model

You can refer to the general Prepare and Quantize 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

Enable oneAPI running environment

source /opt/intel/oneapi/setvars.sh

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

Chosen Device ID	Setting
0	`export ONEAPI_DEVICE_SELECTOR="level_zero:0"` or no action
1	`export ONEAPI_DEVICE_SELECTOR="level_zero:1"`
0 & 1	`export ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"`

Execute

Choose one of following methods to run.

Script

Use device 0:

./examples/sycl/run-llama2.sh 0
# OR
./examples/sycl/run-llama3.sh 0

Use multiple devices:

./examples/sycl/run-llama2.sh
# OR
./examples/sycl/run-llama3.sh

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.

Device selection	Parameter
Single device	--split-mode none --main-gpu DEVICE_ID
Multiple devices	--split-mode layer (default)

Examples:

Use device 0:

ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -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

Use multiple devices:

ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -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

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

I. Setup Environment

Install GPU driver

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

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.

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]

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.

1. Script

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

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-cli

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

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

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:
```
cmake --build build --config Release -j --target llama-cli
```

- 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:

Open the solution in Visual Studio.
Right-click on ggml-sycl and select Properties.
In the left column, expand C/C++ and select DPC++.
In the right panel, find Enable SYCL Offload and set it to Yes.
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 Prepare and Quantize 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

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

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

Chosen Device ID	Setting
0	Default option. You may also want to `set ONEAPI_DEVICE_SELECTOR="level_zero:0"`
1	`set ONEAPI_DEVICE_SELECTOR="level_zero:1"`
0 & 1	`set ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"` or `set ONEAPI_DEVICE_SELECTOR="level_zero:*"`

Execute

Choose one of following methods to run.

Script

examples\sycl\win-run-llama-2.bat

or

examples\sycl\win-run-llama-3.bat

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.

Device selection	Parameter
Single device	--split-mode none --main-gpu DEVICE_ID
Multiple devices	--split-mode layer (default)

Examples:

Use device 0:

build\bin\llama-cli.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

Use multiple devices:

build\bin\llama-cli.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

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

Name	Value	Function
GGML_SYCL	ON (mandatory)	Enable build with SYCL code path.
GGML_SYCL_TARGET	INTEL (default) \| NVIDIA \| AMD	Set the SYCL target device type.
GGML_SYCL_DEVICE_ARCH	Optional (except for AMD)	Set the SYCL device architecture, optional except for AMD. Setting the device architecture can improve the performance. See the table --offload-arch for a list of valid architectures.
GGML_SYCL_F16	OFF (default) \|ON (optional)	Enable FP16 build with SYCL code path. (1.)
GGML_SYCL_GRAPH	ON (default) \|OFF (Optional)	Enable build with SYCL Graph extension.
GGML_SYCL_DNN	ON (default) \|OFF (Optional)	Enable build with oneDNN.
CMAKE_C_COMPILER	`icx` (Linux), `icx/cl` (Windows)	Set `icx` compiler for SYCL code path.
CMAKE_CXX_COMPILER	`icpx` (Linux), `icx` (Windows)	Set `icpx/icx` compiler for SYCL code path.

FP16 is recommended for better prompt processing performance on quantized models. Performance is equivalent in text generation but set GGML_SYCL_F16=OFF if you are experiencing issues with FP16 builds.

Runtime

Name	Value	Function
GGML_SYCL_DEBUG	0 (default) or 1	Enable log function by macro: GGML_SYCL_DEBUG
GGML_SYCL_DISABLE_OPT	0 (default) or 1	Disable optimize features for Intel GPUs. (Recommended to 1 for intel devices older than Gen 10)
GGML_SYCL_DISABLE_GRAPH	0 or 1 (default)	Disable running computations through SYCL Graphs feature. Disabled by default because graph performance isn't yet better than non-graph performance.
GGML_SYCL_DISABLE_DNN	0 (default) or 1	Disable running computations through oneDNN and always use oneMKL.
ZES_ENABLE_SYSMAN	0 (default) or 1	Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory. Recommended to use when --split-mode = layer

Known Issues

Split-mode:[row] is not supported.

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] afer 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:
```
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.

Sugguest reproducing on llama.cpp and report similar issue to llama.cpp. We will surpport 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.

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

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