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Package Details: llama.cpp-sycl b11552-1
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| Git Clone URL: | https://aur.archlinux.org/llama.cpp-sycl.git (read-only, click to copy) |
|---|---|
| Package Base: | llama.cpp-sycl |
| Description: | llama.cpp with Intel Arc GPU acceleration via SYCL/oneAPI. Please read the README on Codeberg before use. |
| Upstream URL: | https://github.com/ggml-org/llama.cpp |
| Keywords: | arc intel llama llama.cpp oneapi openvino sycl vulkan |
| Licenses: | MIT |
| Conflicts: | intel-oneapi-basekit-2025, intel-oneapi-toolkit, llama.cpp, llama.cpp-bin, llama.cpp-bin-noavx, llama.cpp-clblas-git, llama.cpp-clblast, llama.cpp-cublas-git, llama.cpp-cuda, llama.cpp-cuda-essentials-only, llama.cpp-cuda-git, llama.cpp-gfx1151, llama.cpp-git, llama.cpp-hip, llama.cpp-hip-gfx1151, llama.cpp-hipblas-git, llama.cpp-opencl, llama.cpp-openvino, llama.cpp-sycl-f16, llama.cpp-sycl-f16-git, llama.cpp-sycl-f32, llama.cpp-sycl-f32-git, llama.cpp-vulkan, llama.cpp-vulkan-bin, llama.cpp-vulkan-gemma4, llama.cpp-vulkan-git |
| Provides: | llama.cpp |
| Submitter: | cantosun99 |
| Maintainer: | cantosun99 |
| Last Packager: | cantosun99 |
| Votes: | 1 |
| Popularity: | 0.060407 |
| First Submitted: | 2026-05-06 09:00 (UTC) |
| Last Updated: | 2026-10-11 06:03 (UTC) |
Dependencies (8)
- gcc-libs (gcc-libs-gitAUR, gccrs-libs-gitAUR, gcc-libs-fast-optimizedAUR, gcc-libs-snapshotAUR)
- intel-compute-runtime (intel-compute-runtime-legacy-binAUR, intel-compute-runtime-gitAUR, intel-compute-runtime-legacyAUR, intel-compute-runtime-binAUR)
- intel-deep-learning-essentialsAUR
- level-zero-headers (level-zero-headers-gitAUR, level-zero-headers-legacyAUR)
- level-zero-loader (level-zero-loader-gitAUR, level-zero-loader-legacyAUR)
- cmake (cmake3AUR, cmake-gitAUR) (make)
- git (git-gitAUR, git-glAUR, git-wd40AUR) (make)
- make (make-gitAUR, make-staticAUR) (make)
Required by (13)
- assistd (requires llama.cpp) (optional)
- assistd-git (requires llama.cpp) (optional)
- koharu-bin (requires llama.cpp) (optional)
- llamaman-bin (requires llama.cpp) (optional)
- llamastash (requires llama.cpp) (optional)
- llamastash-bin (requires llama.cpp) (optional)
- llamastash-git (requires llama.cpp) (optional)
- manboster (requires llama.cpp) (optional)
- manboster-bin (requires llama.cpp) (optional)
- manboster-git (requires llama.cpp) (optional)
- voxd (requires llama.cpp) (optional)
- voxd-bin (requires llama.cpp) (optional)
- voxd-git (requires llama.cpp) (optional)
Latest Comments
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cantosun99 commented on 2026-09-13 21:16 (UTC)
@Muflone Thank you!
Muflone commented on 2026-09-13 20:39 (UTC)
@cantosun99 thanks for clarifying, I'll discard the deletion request
cantosun99 commented on 2026-09-13 20:37 (UTC)
@Muflone Well the difference is quite simple, this is llama.cpp but compiled for the SYCL backend for Intel Arc GPUs. Llama.cpp + ggml-sycl has nothing to do with this package, that's like saying a laptop with an SoC is the same as one with a dGPU, so quite frankly, that deletion request is absolutely pointless.
Muflone commented on 2026-09-13 18:42 (UTC)
@cantosun99 I see you updated this package today
What is the difference with llama-cpp + ggml-sycl ?
there's a deletion request pending
cantosun99 commented on 2026-07-11 07:01 (UTC)
If you encounter issues with the intel-deep-learning-essentials while installing this package, 99% of the time it's one of the following options:
If you previously or currently have the
intel-oneapi-toolkit,intel-oneapi-basekit-2025orintel-oneapi-hpckitpackages installed, please uninstall them and verify that everything in/opt/intelis deleted. Most likely you don't even need everything that's included in the full Intel® oneAPI Toolkit for developers, that's why the Intel® Deep Learning Essentials for users exists. I suggest replacing the three packages with this one unless you are one of the few people that need the full kit.If you are updating this package and it fails, you have to delete
/home/yourusername/inteland/home/yourusername/.inteland retry the update. Please verify just in case, but there usually is nothing important in there anyway.Please visit https://github.com/cantosun99/intel-deep-learning-essentials and https://github.com/cantosun99/llama.cpp-sycl to stay up to date.
cantosun99 commented on 2026-07-05 11:05 (UTC)
Important: SYCL now supports MTP and beats llama.cpp-vulkan in PP by ~150 tok/s while only being ~1.5 tok/s slower in TG.
Also SYCL has gotten a new release on the 1st of July with the Intel Intel® Deep Learning Essentials now being on version 2026.1.0 and the Intel® Deep Neural Network Library on 2026.0.1 so I expect the future llama.cpp releases to further optimize the speed of the SYCL backend.
Benchmarks ran with llama.cpp b9873 on a Sparkle Intel Arc Pro B70 passive using Unsloth's Qwen3.6 27B Q6_K on Arch Linux x86_64 with the Linux 7.1.2-3-cachyos kernel.
Since llama-bench doesn't support MTP, I used my recommended daily-usage flags, warmed the model up with "Say hi." and then benchmarked it for prompt processing with "Read the following text and then only reply with "ok" once you have done so." followed by the German Wikipedia entry for Black Sabbath converted to text. For token generation I also warmed it up with "Say hi." and benchmarked it with "Write a complete Snake game in a single Python file. Output only the code, no explanations, no commentary. Include: arrow key controls, collision detection, food spawning, score display, and a game over screen. Make it actually runnable." with thinking off.
Backend MTP PP TG
Vulkan off 317.32 tok/s 16.92 tok/s
Vulkan on 313.88 tok/s 33.40 tok/s
SYCL off 485.18 tok/s 15.75 tok/s
SYCL on 423.76 tok/s 31.64 tok/s
cantosun99 commented on 2026-05-18 12:55 (UTC)
Actually, I realized that the way the Intel installer works, the two packages will not be able to coexist together so I'll have to bundle the DNN into the DLE which used to be the way it was shipped anyway up until three weeks ago. Otherwise they just conflict with eachother and wouldn't even be removable by pacman anyway since they are both written into the same folder. So yeah, I made the two packages too soon, the seperate oneDNN has to go, my bad.
cantosun99 commented on 2026-05-18 11:40 (UTC)
So, I hope that this finally fixes everything you were criticising. Again, I'm just trying to do my best and learn from my mistakes.
Now I added the Intel DLE and DNN as two seperate packages and let the llama.cpp-sycl package use them as dependencies. The two packages have the 2026.0.0 as their version number and the llama.cpp-sycl now has the used llama.cpp release tag as the version number since it now doesn't always get the latest release (I misread what you meant when you criticised the package building against head, apologies for that), just like the other builds, llama.cpp-cuda or llama.cpp-vulkan do as well.
I know you will point out that a version of oneDNN already exists, but it pulls directly from the most current GitHub release and for this specific use-case it's absolutely critical, that both the DLE and DNN come directly from Intel in the same release (2026.0.0 at the time of writing) to retain compatibility. I think this is a valid reason to make another DNN package as what it actually provides, more specifically WHERE it gets it from, is vastly different and required for this to work.
I hope this finally fixes every complaint that you had, hcartiaux, and is a valid reason enough for these now three packages. Everything else like the intel-compute-runtime or the levelzero stuff isn't critical and I used the existing packages before, but these two HAVE to be these specific versions. And again, I do think that it's a valid goal to have the install be 10 GB less than the full kit, especially considering the costs of drives nowadays and having the specific llama.cpp version for Intel GPUs being up to date by someone that actually uses it daily.
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