mirror of
https://github.com/LostRuins/koboldcpp.git
synced 2026-08-21 14:25:35 +00:00
mirror of https://github.com/LostRuins/koboldcpp
2e2d99cfd2
* ci: Add support for CUDA 13.4 ARM64 builds for Windows Added an architecture-specific CUDA 13.4 Windows build entry targeting ARM64. Added a CMake configuration to enable ARM64 CUDA cross-compilation from an x64 Windows environment using the x64-hosted CUDA and MSVC toolchain while linking against the ARM64 CUDA import libraries to produce ggml-cuda.dll. Validated the self-hosted Windows x64 workflow, including toolkit acquisition, CMake configuration, ARM64 CUDA cross-compilation, and packaging. Runtime validation was performed separately on a native ARM64 RTX Spark system using TinyLlama 1.1B Q4_K_M to verify the generated binaries. The ARM64 CUDA job builds only the ggml-cuda.dll backend (LLAMA_BUILD_SERVER=OFF). The release consists of two packages: the main ARM64 release package, which combines the existing ARM64 CPU outputs with ggml-cuda.dll, and a separate runtime package containing the required CUDA runtime libraries (cudart64_13.dll, cublas64_13.dll, and cublasLt64_13.dll). The CUDA 13.4 setup uses NVIDIA Developer Preview component archives instead of the GA component downloads used by the existing CUDA setups and will require updates once CUDA 13.4 reaches GA. * ci: cleans up to align with x64 CUDA setup - Moves CUDA-specific CMake options into matrix defines. - Keeps the CUB 3DOT2 option only for CUDA 12.4. - Removes runtime argument construction and the unnecessary server option. - Aligns ARM64 CUDA runtime packaging with the existing robocopy approach. - Generalizes the ARM64 release label from CUDA 13.4 to CUDA 13. * ci: Set CUDA job name as version-architecture pair * mark as preview Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> |
||
|---|---|---|
| .devops | ||
| .gemini | ||
| .github | ||
| .pi/gg | ||
| app | ||
| benches | ||
| ci | ||
| cmake | ||
| common | ||
| conversion | ||
| docs | ||
| examples | ||
| ggml | ||
| gguf-py | ||
| grammars | ||
| include | ||
| licenses | ||
| media | ||
| models | ||
| pocs | ||
| requirements | ||
| scripts | ||
| skills | ||
| src | ||
| tests | ||
| tools | ||
| vendor | ||
| .clang-format | ||
| .clang-tidy | ||
| .dockerignore | ||
| .ecrc | ||
| .editorconfig | ||
| .flake8 | ||
| .gitignore | ||
| .gitmodules | ||
| .pre-commit-config.yaml | ||
| AGENTS.md | ||
| AUTHORS | ||
| build-xcframework.sh | ||
| CLAUDE.md | ||
| CMakeLists.txt | ||
| CMakePresets.json | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| convert_hf_to_gguf.py | ||
| convert_hf_to_gguf_update.py | ||
| convert_llama_ggml_to_gguf.py | ||
| convert_lora_to_gguf.py | ||
| flake.nix | ||
| LICENSE | ||
| Makefile | ||
| mypy.ini | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| README.md | ||
| requirements.txt | ||
| SECURITY.md | ||
| ty.toml | ||
llama.cpp
LLM inference in C/C++
manifesto / ggml / ops / maintainer PRs / compile times / 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
|
|
|
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 [In Progress] | 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
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 - stb-image - Single-header image format decoder, used by multimodal subsystem - Public domain
- nlohmann/json - Single-header JSON library, used by various tools/examples - MIT License
- miniaudio.h - Single-header audio format decoder, used by multimodal subsystem - Public domain
- subprocess.h - Single-header process launching solution for C and C++ - Public domain