mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-10-28 17:56:13 +08:00
Michael Taylor
7170cad4d0
* sycl: fix CONV_2D wrong results for non-contiguous input The kernel indexes the `input` as a contiguous [W, H, C, N] tensor, but supports_op claimed the op for any layout. A non-contiguous (e.g. `CWHN` permuted) input was read with the wrong element offsets, silently producing wrong results. Gate the CONV_2D op on `input` and `kernel` contiguity, matching the CUDA backend contract, and assert input contiguity in the op. `test-backend-ops -b SYCL0 -o CONV_2D`: goes from 16 failures to 0, none. ```text 2006/2006 tests passed Backend SYCL0: OK Backend 2/2: CPU Skipping 2/2 backends passed OK ``` Assisted-by: pi (unsloth/Qwen3.8-27B-GGUF:Q4_K_M via llama.cpp) * sycl : CONV_2D cwhn=1 support Make the SYCL backend compute `GGML_OP_CONV_2D` correctly for `cwhn=1` (CWHN channel-order) inputs and kernels, so the previously failing test- backend-ops CONV_2D cases pass on SYCL instead of being skipped or miscomputed. - Implement CWHN layout support in `ggml/src/ggml-sycl/conv2d.cpp` (`ggml_sycl_op_conv2d`). - Update SYCL `supports_op` for `GGML_OP_CONV_2D` in `ggml/src/ggml-sycl/ggml-sycl.cpp` to accept both layouts. Assisted-by: pi (Greatjedi/Qwen3.8-27B-Intel-Arc-Tuned-GGUF via llama.cpp) * sycl docs : update ops for 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:
# curl
curl -LsSf https://llama.app/install.sh | sh
# powershell
irm https://llama.app/install.ps1 | iex
- 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