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https://github.com/ggml-org/llama.cpp.git
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Yuri Khrustalev
fcc891545b
which resulted in different greedy transcripts for 4.5% of English and 6.5% of Japanese
test utterances. In Japanese, some differences changed entire words.
This change:
* uses `log(x + 2^-24)` instead of clamping to the log floor
* uses a symmetric Hann window, equivalent to `torch.hann_window(periodic=False)`
* adds the normalization epsilon to the standard deviation instead of inside the square root
Only the `lfm2a` preprocessor opts into these behaviors. Other audio preprocessors are unchanged.
Tested on top of 84e76d8 using `llama-server` with CUDA and `temperature=0`, compared against
http://github.com/Liquid4All/liquid-audio fp32.
Test set:
* 200 LibriSpeech `test-clean` utterances (EN)
* 200 Common Voice `ja` test utterances (JP)
* identical 16 kHz audio passed to both implementations
| Greedy transcript identical to `liquid-audio` | Without fix | With fix |
| --------------------------------------------- | ----------: | ----------: |
| EN F16 | 191/200 | 200/200 |
| JP F32 | 187/200 | 200/200 |
| JP F16 | 187/200 | 199/200 |
The remaining JP F16 difference is a comma and matches the reference implementation's own bf16
output.
Mel relative L2 error versus `liquid-audio`:
* EN: 3.2% -> ~2e-6 median
* JP: 3.9% -> ~2e-6 median
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