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The SYCL FWHT covers 64 to 512 via the standard butterfly network, plus 384/640/768/1280 via the Kronecker/Paley construction added separately in Hadamard hint can produce (1024, 2048, 4096, 8192); those still fall through to the default case and run as a dense GEMM against the materialized rotation tensor, correct but O(n^2) instead of O(n log n). fwht_kernel_wide runs one row per work-group instead of per sub-group, so each work-item keeps N/NT values rather than N/WARP_SIZE. Butterflies below the sub-group width still shuffle; those up to the work-group width go through work-group local memory; the rest stay in registers. Same butterfly and sign convention as the existing narrow kernel. ggml's SYCL backend registration (dpct::dev_mgr) unconditionally requires a GPU-labeled platform to exist and throws before any op-level test can run, so test-backend-ops could not be exercised on this box (a GPU-less pod) even via the CPU device. Verified instead with a standalone harness: the same kernel body run through a real SYCL CPU device (Intel oneAPI DPC++ 2026.1, OpenCL CPU backend), checked against an independent recursive-doubling Hadamard reference, cross-validated by first running the existing unmodified narrow kernel through the identical harness and confirming it passes (rules out a reference-convention bug before trusting a pass on the new code). Random-input results for all four widths, single- and multi-row: N=1024 NT=256 rows=1 max_abs_err=1.7e-07 max_rel_err=4.9e-04 PASS N=2048 NT=256 rows=1 max_abs_err=1.9e-07 max_rel_err=2.0e-04 PASS N=4096 NT=256 rows=1 max_abs_err=2.0e-07 max_rel_err=1.4e-04 PASS N=8192 NT=256 rows=1 max_abs_err=2.5e-07 max_rel_err=3.8e-03 PASS N=1024 NT=256 rows=7 max_abs_err=2.4e-07 max_rel_err=1.0e-03 PASS N=2048 NT=256 rows=5 max_abs_err=3.0e-07 max_rel_err=9.4e-04 PASS N=4096 NT=256 rows=3 max_abs_err=2.7e-07 max_rel_err=1.7e-03 PASS N=8192 NT=256 rows=2 max_abs_err=2.5e-07 max_rel_err=1.9e-03 PASS This covers the kernel algorithm itself; it does not exercise the ggml dispatch/supports_op integration end to end, which needs a real GPU (or a SYCL GPU plugin) to get past backend registration. test-backend-ops build is verified: fwht.cpp recompiles with zero warnings as part of ggml-sycl.
llama.cpp
LLM inference in C/C++
ggml / ops / maintainer PRs / dev stats / lib llama API / llama-server REST API
Quick start
A few options to get llama.cpp installed on your machine:
- Visit https://llama.app and follow the instructions
- Run with Docker - see our Docker documentation
- Download pre-built binaries from the releases page
- Build from source by cloning this repository - check out our build guide
Once installed:
# Download and run a model directly from Hugging Face
llama cli -hf ggml-org/Qwen3.5-0.8B-GGUF
# Launch OpenAI-compatible API server
llama serve -hf ggml-org/Qwen3.5-0.8B-GGUF
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Description
The main goal of llama.cpp is to enable LLM (and VLM) inference with minimal setup and state-of-the-art performance on
a wide range of hardware - locally and in the cloud.
- Plain C/C++ implementation without any dependencies
- Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
- AVX, AVX2, AVX512 and AMX support for x86 architectures
- RVV, ZVFH, ZFH, ZICBOP and ZIHINTPAUSE support for RISC-V architectures
- 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads GPUs via MUSA)
- Vulkan and SYCL backend support
- CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity
The llama.cpp project is build on top of the ggml library.
Supported backends
| Backend | Target devices |
|---|---|
| BLAS | All |
| BLIS | All |
| CANN | Ascend NPU |
| CUDA | Nvidia GPU |
| HIP | AMD GPU |
| Hexagon | Snapdragon |
| IBM zDNN | IBM Z & LinuxONE |
| MUSA | Moore Threads GPU |
| Metal | Apple Silicon |
| OpenCL | Adreno GPU |
| OpenVINO [In Progress] | Intel CPUs, GPUs, and NPUs |
| RPC | All |
| SYCL | Intel GPU |
| VirtGPU | VirtGPU APIR |
| Vulkan | GPU |
| WebGPU | All |
| ZenDNN | AMD CPU |
Documentation
Tools
Development
- How to build
- Running on Docker
- Build on Android
- Multi-GPU usage
- Performance troubleshooting
- GGML tips & tricks
- XCFramework
- Completions
- Models
- Release process
Contributing
- Contributors can open PRs
- Collaborators will be invited based on contributions
- Maintainers can push to branches in the
llama.cpprepo and merge PRs into themasterbranch - Any help with managing issues, PRs and projects is very appreciated!
- Read the CONTRIBUTING.md for more information
Acknowledgements
- yhirose/cpp-httplib - Single-header HTTP server, used by
llama-server- MIT license - nothings/stb - Single-header image format decoder, used by multimodal subsystem - Public domain
- nlohmann/json - Single-header JSON library, used by various tools/examples - MIT License
- mackron/miniaudio - Single-header audio format decoder, used by multimodal subsystem - Public domain
- sheredom/subprocess.h - Single-header process launching solution for C and C++ - Public domain