PascalandXuan-Son Nguyen
a7fb71fab8
* log, server: make router child lines carry their own colors The logger writes the color reset after the trailing newline, so the reset opens the next line. On the shared pipe of a router child it lands in front of the next state command, which the router then misses, and the line break that works around it shows up as an empty log line on every progress update. The reset now goes before the trailing newlines, so every line is self contained and the command goes back to its plain framing. The router passes its effective color setting to its children, whose output ends up in its terminal, and leaves that option out when comparing presets on reload. * log: enable ANSI colors on the Windows console A Windows console renders ANSI sequences only in virtual terminal mode, which nothing turns on for the logger, so llama-server prints raw escape codes on the Windows 10 console while llama-cli, whose console code enables it, shows colors. The logger now enables virtual terminal mode on stdout and stderr when it turns colors on, and keeps colors off when a console cannot render them. Pipes and files take the sequences as is. * server: separate the router child commands from its logs The child sent its state commands on the same pipe as its logs, so the router had to pick them out of the log stream by a line prefix, and any unterminated write in front of a command made the router miss it. This resolves the TODO at the spawn that called for splitting stdout and stderr. The child now keeps stdout for the commands and points everything else written to stdout at stderr, before anything is written. The router reads both pipes, handles the commands from stdout and forwards stderr as the log, and warns about any other line on the command pipe. * server: address review from ngxson The single server_child is now created first in the entry point and its constructor keeps stdout for the commands, so the stream is a member of the instance instead of a static, and init() is gone. The instance is passed down to the server, while the CLI entry point creates its own. * Update tools/server/server.cpp --------- Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>
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