mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-09-25 23:47:23 -05:00
Georgi Gerganov
4c5957c277
* test-save-load-state : print a per-model results table in --models mode in --models mode the output was very heavy: every model printed its token dumps, per-test headers and PASS lines. instead, silence all logging except the table itself (common_log_set_verbosity_thold(0) leaves only LOG / LOG_LEVEL_OUTPUT) and print one row per model with one column per test, colored PASS/FAIL/SKIP cells, row by row. - run_save_load_tests_for_model returns a test_suite with a dynamic std::vector<test_status> and continues past failures: tests 3-5 are SKIPped when the baseline (test 1) fails, model init failure skips all - per-test token dumps, test headers and PASS lines are demoted to LOGV(LOG_LEVEL_INFO, ...) so they still show in single-model mode - the table header/rows derive their columns from test_names; the model name is printed and flushed before the suite runs so the model currently in flight is always visible - single-model output and exit codes are unchanged Assisted-by: pi:llama.cpp/Qwen3.8-27B * test-save-load-state : print example usage on -h add a print_usage callback passed to common_params_parse, so -h/--help also shows example commands for the tool-specific --models option and the -lv verbosity level Assisted-by: pi:llama.cpp/Qwen3.8-27B * test-save-load-state : remove comments ref: https://github.com/ggml-org/llama.cpp/pull/29316 Assisted-by: pi:llama.cpp/Qwen3.8-27B
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