llama.cpp/docs/backend/SYCL.md

llama.cpp for SYCL

• Background
• Recommended Release
• News
• OS
• Hardware
• Performance Reference
• 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

Windows

The following releases are verified and recommended:

Commit ID	Tag	Release	Verified Platform	Update date
24e86cae72	b5377	llama-b5377-bin-win-sycl-x64.zip	Arc B580/Linux/oneAPI 2025.1 LNL Arc GPU/Windows 11/oneAPI 2025.1.1	2025-05-15
3bcd40b3c5	b4040	llama-b4040-bin-win-sycl-x64.zip	Arc A770/Linux/oneAPI 2024.1 MTL Arc GPU/Windows 11/oneAPI 2024.1	2024-11-19
fb76ec31a9	b3038	llama-b3038-bin-win-sycl-x64.zip	Arc A770/Linux/oneAPI 2024.1 MTL Arc GPU/Windows 11/oneAPI 2024.1

Ubuntu 24.04

The release packages for Ubuntu 24.04 x64 (FP32/FP16) only include the binary files of the llama.cpp SYCL backend. They require the target machine to have pre-installed Intel GPU drivers and oneAPI packages that are the same version as the build package. To get the version and installation info, refer to .github/workflows/release.yml#L713: ubuntu-24-sycl -> Download & Install oneAPI.

It is recommended to use them with Intel Docker.

The packages for FP32 and FP16 would have different accuracy and performance on LLMs. Please choose it according to the test result.

News

• 2026.04-05

  • Optimize mul_mat by reorder feature for data type: Q4_K, Q5_K, Q6_K, Q8_0.
  • Fused MoE.
  • Upgrate CI and built package for oneAPI 2025.3.3, support Ubuntu 24.04 built package.

• 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

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).

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

Intel GPU	Status	Verified Model
Intel Data Center Max Series	Support	Max 1550, 1100
Intel Data Center Flex Series	Support	Flex 170
Intel Arc A-Series	Support	Arc A770, Arc A730M, Arc A750
Intel Arc B-Series	Support	Arc 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-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

Performance Reference

To get the supported LLMs, GPUs, and performance reference, please check Performance of llama.cpp on Intel GPU with SYCL backend.

You could update your test result in it directly.

Docker

Please refer to Docker with SYCL for details.

Quick Development WOW

This chapter is for quick development & try with SYCL backend on Intel GPU.

You need to install following sofeware before development:

• Intel GPU driver
• oneAPI package
• other development tools.

Please refer to Linux or Windows for above installation and resolve the trouble in usage. There are the detailed guide.

• Linux

## build from source code
./examples/sycl/build.sh

## run CONV_2D_DW unit test cases
./build/bin/test-backend-ops -b SYCL0 -o CONV_2D_DW

## run all unit test cases
./build/bin/test-backend-ops -b SYCL0

## run with LLM on the first GPU
./examples/sycl/test.sh -mg 0 -m xxxx.gguf

## run service with LLM on the first GPU
export ONEAPI_DEVICE_SELECTOR="level_zero:0"
./examples/sycl/start-svr.sh -m xxxx.gguf

## update the docs/ops.md for new/update OPs
./examples/sycl/update-ops-doc.sh

• Windows

## build from source code
examples\sycl\win-build-sycl.bat

## run CONV_2D_DW unit test cases
build\bin\test-backend-ops.exe -b SYCL0 -o CONV_2D_DW

## run all unit test cases
build\bin\test-backend-ops.exe -b SYCL0

## run LLM on the first GPU
examples\sycl\win-test.bat -mg 0 -m xxxx.gguf

## run service with LLM on the first GPU
set ONEAPI_DEVICE_SELECTOR="level_zero:0"
examples\sycl\win-start-svr.bat -m xxxx.gguf

## update the docs/ops.md for new/update OPs
examples\sycl\win-update-ops-doc.bat

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.

Verified release
2025.3.3
2025.2.1
2025.1
2024.1

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

# Uses FP32, consider using FP16 for better performance in most cases
./examples/sycl/build.sh

or

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

# Option 1: Use FP16 (recommended for better performance in most cases)
cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON

# Option 2: Use FP32
cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx

# 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

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.

1. Script

• Use device 0:

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

• Use multiple devices:

./examples/sycl/test.sh

• Run llama-server:

./examples/sycl/start-svr.sh -m PATH/MODEL_FILE

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.

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

--split-mode tensor (tensor parallelism) shards each layer across the selected GPUs. It requires flash attention, which is auto-enabled when --flash-attn is left at its default auto, so --split-mode tensor works out of the box. Passing --flash-attn off together with --split-mode tensor is rejected at context creation. The default f16 KV cache is recommended. Tensor parallelism is currently optimized for 2 GPUs; other device counts fall back to a generic all-reduce.

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

# Uses FP32, consider using FP16 for better performance in most cases
.\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 FP16 (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 -DGGML_SYCL_F16=ON

# Option 2: Or FP32
cmake -B build -G "Ninja" -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release

cmake --build build --config Release -j

Or, use CMake presets to build:

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-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:

  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

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.

1. Script

• Run test:

examples\sycl\win-test.bat

• Run llama-server:

examples\sycl\win-start-svr.bat -m PATH\MODEL_FILE

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.

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

--split-mode tensor (tensor parallelism) shards each layer across the selected GPUs. It requires flash attention, which is auto-enabled when --flash-attn is left at its default auto, so --split-mode tensor works out of the box. Passing --flash-attn off together with --split-mode tensor is rejected at context creation. The default f16 KV cache is recommended. Tensor parallelism is currently optimized for 2 GPUs; other device counts fall back to a generic all-reduce.

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

Name	Value	Function
GGML_SYCL	ON (mandatory)	Enable build with SYCL code path.
GGML_SYCL_TARGET	INTEL (default)	Set the SYCL target device type.
GGML_SYCL_DEVICE_ARCH	Optional	Set the SYCL device architecture. 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.
GGML_SYCL_HOST_MEM_FALLBACK	ON (default) |OFF (Optional)	Allow host memory fallback when device memory is full during quantized weight reorder. Enables inference to continue at reduced speed (reading over PCIe) instead of failing. Requires Linux kernel 6.8+.
GGML_SYCL_SUPPORT_LEVEL_ZERO_API	ON (default) |OFF (Optional)	Support to use Level Zero API for device memory allocation. Requires Level Zero headers/library at build time and Intel GPU driver (Level Zero runtime) at run time. Reduces system RAM usage during multi-GPU inference. SYCL backend always runs on Level Zero running time even if it's set as OFF (The SYCL api will be usage for memory allocation).
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.

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

Name	Value	Function
GGML_SYCL_DEBUG	0 (default) or 1	Enable log function by macro: GGML_SYCL_DEBUG
GGML_SYCL_DEV2DEV_MEMCPY	0 (default), 1, 2	Choose the method of dev2dev memory copy. Value: * 0: SYCL API (default), only support dGPUs. * 1: L0 API -- Better performance, only support dGPUs, found to lead to abnormal crash in some case. * 2: Host Forward -- Most stable method for all cases (including iGPU + dGPU*N), but with lower performance (-2% to -5%). SYCL & L0 API are easy to be impacted by Intel GPU driver issue. When you meet the garbled output or crash issues in multiple GPUs case, try with this debug flag to work around or check the issue.
GGML_SYCL_ENABLE_FLASH_ATTN	1 (default) or 0	Enable Flash-Attention. It can reduce memory usage. The performance impact depends on the LLM.
GGML_SYCL_ENABLE_OPT	0 or 1 (default)	Enable optimize features for Intel GPUs. (Recommended to 0 for Intel devices older than Gen 10)
GGML_SYCL_ENABLE_GRAPH	0 (default) or 1	Enable running computations through SYCL Graphs feature. Disabled by default because SYCL Graph is still on development, no better performance.
GGML_SYCL_USE_LEVEL_ZERO_API	1 (default) or 0	Use Level Zero API for device memory allocation instead of SYCL. Reduces system RAM usage on Intel dGPUs by avoiding DMA-buf/TTM host memory staging. Requires GGML_SYCL_SUPPORT_LEVEL_ZERO_API=ON at build time. SYCL backend always runs on Level Zero running time even if it's set as OFF (The SYCL api will be usage for memory allocation).
GGML_SYCL_ENABLE_DNN	0 or 1 (default)	Enable running computations through oneDNN and always use oneMKL.
GGML_SYCL_FA_ONEDNN	1 (default) or 0	Enable the oneDNN fused SDPA (flash-attention) path on supported GPUs. Set to 0 to always use the native SYCL flash-attention kernel.
GGML_SYCL_FA_ONEDNN_MAX_KV	0 (default, disabled) or positive integer	By default (0), all sequences are handled by the oneDNN fused SDPA path, regardless of KV length; a positive value caps that length, past which sequences fall back to the native kernel. If GPU driver watchdog resets (DEVICE_LOST) occur during long-context inference, set this near the context depth where they start, e.g. 24576.
GGML_SYCL_ENABLE_VMM	0 or 1 (default)	Enable the virtual-memory device pool.
GGML_SYCL_ENABLE_MKL_FA	1 (default) or 0	Enable oneMKL GEMM flash attention for XMX-accelerated prompt processing with quantized KV cache. Automatically activates during prefill (prompt processing) when all conditions are met: (1) flash-attn enabled (-fa or --flash-attn on), (2) KV cache quantized (--cache-type-k q8_0 --cache-type-v q8_0 or other *_0/*_1 types), (3) batch size ≥ 1024 (--batch-size 1024), (4) prompt length ≥ 1024 tokens. Set to 0 to force the TILE kernel for A/B testing. Example minimum command: llama-cli -m model.gguf -fa -ngl 99 --cache-type-k q8_0 --cache-type-v q8_0 --batch-size 1024 -p "your prompt"
GGML_SYCL_MKL_FA_DEBUG	0 (default) or 1	Enable per-call diagnostic logging for MKL flash attention: GEMM/softmax timings, interleaved-head detection, and buffer memory usage.
GGML_SYCL_MKL_FA_DIAG	0 (default) or 1	Enable output fingerprinting for MKL flash attention. Dumps the first 64 float output values for the first 6 FA calls with n_kv ≥ 1024, labeled with kernel type (MKL/TILE/VEC) for cross-kernel comparison.
GGML_SYCL_ENABLE_FUSION	0 or 1 (default)	Enable fused-kernel dispatch in graph compute (currently top-k MoE gating).
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
UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS	0 (default) or 1	Allow SYCL/Unified Runtime Level Zero device allocations larger than 4 GiB. llama.cpp's direct Level Zero allocation path requests the relaxed maximum-size limit itself when GGML_SYCL_ENABLE_LEVEL_ZERO=1.
GGML_SYCL_USM_SYSTEM	0 (default) or 1	Enable experimental support for USM system allocations for large GPU buffers. This requires enough host memory for model weights and caches, an Intel Xe2+ GPU such as BMG or newer and supported on Linux only, with CONFIG_DRM_XE_GPUSVM enabled.

Compile-time Flags

Pass these via CXXFLAGS or add a one-off #define to enable a flag on the spot.

Name	Function
DEBUG_SYCL_POOL	Enable device memory pool logging on teardown. Useful for profiling allocations.
DEBUG_SYCL_MALLOC	Enable verbose per-call logging of device pool alloc/free operations.
GGML_SYCL_SUPPORT_VMM	Support to building with VMM code. Default is Yes.

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 building.

  • Good: Builds 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:

  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

  With the default GGML_SYCL_ENABLE_LEVEL_ZERO=1, llama.cpp requests Level Zero's relaxed maximum-size allocation limit directly. If Level Zero support is disabled at build time or runtime and the allocation goes through SYCL/Unified Runtime instead, enable support for allocations larger than 4 GiB by:

    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