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Improve SYCL doc #23025

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e67b3aa
Tidy up SYCL doc a bit
malsbat May 11, 2026
4bf20d9
Correct documented default for GGML_SYCL_GRAPH
malsbat May 11, 2026
e25af0b
Move docker instructions from SYCL.md to docker.md
malsbat May 12, 2026
0e8aed1
Refer to intel.Dockerfile for ARGs and their defaults
malsbat Jun 1, 2026
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Remove mention of Nvidia in SYCL row of backend table
malsbat Jun 1, 2026
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52 changes: 12 additions & 40 deletions docs/backend/SYCL.md
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Expand Up @@ -43,11 +43,11 @@ The following releases are verified and recommended:

### 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 release.yml: ubuntu-24-sycl -> Download & Install oneAPI.
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](../../.github/workflows/release.yml#L713): ubuntu-24-sycl -> Download & Install oneAPI.

It is recommended to use them with Intel Docker.
It is recommended to use them with [Intel Docker](https://hub.docker.com/r/intel/deep-learning-essentials).

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

## News

Expand Down Expand Up @@ -152,35 +152,7 @@ NA

## Docker

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

### Build image

```sh
# 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](../docker.md) to see the available images.

### Run container

```sh
# 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](#linux) for details)*.
Please refer to [Docker with SYCL](../docker.md#docker-with-sycl) for details.

## Linux

Expand All @@ -190,7 +162,7 @@ docker run -it --rm -v "/path/to/models:/models" --device /dev/dri/renderD128:/d

- **Intel GPU**

Intel data center GPUs drivers installation guide and download page can be found here: [Get intel dGPU Drivers](https://dgpu-docs.intel.com/driver/installation.html#ubuntu-install-steps).
Intel data center GPUs drivers installation guide and download page can be found here: [Get Intel dGPU Drivers](https://dgpu-docs.intel.com/driver/installation.html#ubuntu-install-steps).

*Note*: for client GPUs *(iGPU & Arc A-Series)*, please refer to the [client iGPU driver installation](https://dgpu-docs.intel.com/driver/client/overview.html).

Expand Down Expand Up @@ -240,7 +212,7 @@ Please follow the instructions for downloading and installing the Toolkit for Li

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.
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|
|-|
Expand Down Expand Up @@ -319,7 +291,7 @@ 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:
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:

Expand Down Expand Up @@ -465,7 +437,7 @@ In the oneAPI command line, run the following to print the available SYCL device
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:
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):
```
Expand Down Expand Up @@ -717,7 +689,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
| 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](https://github.com/intel/llvm/blob/sycl/sycl/doc/design/OffloadDesign.md#--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 | OFF *(default)* \|ON *(Optional)* | Enable build with [SYCL Graph extension](https://github.com/intel/llvm/blob/sycl/sycl/doc/extensions/experimental/sycl_ext_oneapi_graph.asciidoc). |
| GGML_SYCL_GRAPH | ON *(default)* \|OFF *(Optional)* | Enable build with [SYCL Graph extension](https://github.com/intel/llvm/blob/sycl/sycl/doc/extensions/experimental/sycl_ext_oneapi_graph.asciidoc). |
| 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+. |
| CMAKE_C_COMPILER | `icx` *(Linux)*, `icx/cl` *(Windows)* | Set `icx` compiler for SYCL code path. |
Expand All @@ -731,7 +703,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
|-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------|
| GGML_SYCL_DEBUG | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG |
| 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_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_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 SYCL Graph is still on development, no better 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.<br>Recommended to use when --split-mode = layer |
Expand Down Expand Up @@ -773,8 +745,8 @@ Pass these via `CXXFLAGS` or add a one-off `#define` to enable a flag on the spo

- `Split-mode:[row]` is not supported.

- Missed the AOT (Ahead-of-Time) in buiding.
- Good: build quickly, smaller size of binary file.
- 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
Expand Down
44 changes: 44 additions & 0 deletions docs/docker.md
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Expand Up @@ -140,3 +140,47 @@ docker run -v /path/to/models:/models local/llama.cpp:full-musa --run -m /models
docker run -v /path/to/models:/models local/llama.cpp:light-musa -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
docker run -v /path/to/models:/models local/llama.cpp:server-musa -m /models/7B/ggml-model-q4_0.gguf --port 8080 --host 0.0.0.0 -n 512 --n-gpu-layers 1
```

## Docker With SYCL

## Building Docker locally

```bash
docker build -t local/llama.cpp:full-intel --target full -f .devops/intel.Dockerfile .
docker build -t local/llama.cpp:light-intel --target light -f .devops/intel.Dockerfile .
docker build -t local/llama.cpp:server-intel --target server -f .devops/intel.Dockerfile .
```

You may want to pass in some different `ARGS`, depending on the SYCL environment supported by your container host, as well as the GPU architecture.

The defaults are:
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- `GGML_SYCL_F16` set to `OFF`
- `IGC_VERSION` set to `v2.30.1`
- `IGC_VERSION_FULL` set to `2_2.30.1+20950`
- `COMPUTE_RUNTIME_VERSION` set to `26.09.37435.1`
- `COMPUTE_RUNTIME_VERSION_FULL` set to `26.09.37435.1-0`
- `IGDGMM_VERSION` set to `22.9.0`

The resulting images, are essentially the same as the non-SYCL images:

1. `local/llama.cpp:full-intel`: This image includes both the `llama-cli` and `llama-completion` executables and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.
2. `local/llama.cpp:light-intel`: This image only includes the `llama-cli` and `llama-completion` executables.
3. `local/llama.cpp:server-intel`: This image only includes the `llama-server` executable.

## Usage

After building locally, usage is similar to the non-SYCL examples, but you'll need to add the `--device` flag.

```bash
# 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/card0).
docker run --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card0:/dev/dri/card0 -v /path/to/models:/models local/llama.cpp:full-intel -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 99
docker run --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card0:/dev/dri/card0 -v /path/to/models:/models local/llama.cpp:light-intel -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 99
docker run --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card0:/dev/dri/card0 -v /path/to/models:/models local/llama.cpp:server-intel -m /models/7B/ggml-model-q4_0.gguf --port 8080 --host 0.0.0.0 -n 512 --n-gpu-layers 99
```

*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](./backend/SYCL.md#linux) for details)*.