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Xuan-Son NguyenandPascal
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* server: add realtime transcription API * drop transcription_sessions * proxy ws * add docs * server: let the API key middleware guard the websocket httplib runs the pre-routing handler before the websocket upgrade, so the API key middleware answers 401 at handshake like any other route. This covers the router proxy, whose children run keyless, and accepts the same key forms as HTTP (Bearer, raw key, X-Api-Key). * server: keep realtime slots out of the prompt loop A realtime slot waiting for audio is GENERATING with nothing to decode, so it fell through the prompt loop and took slot_batched. A request on a higher slot then failed can_batch_with on the task type and starved for the whole session. Realtime slots have no prompt: the loop sets them up once and always leaves them. * server: start the realtime session once its slot is live The audio stream was created in the prompt loop, which skips a slot that cannot batch with the one generating, and session.created went out before the task was posted: early appends failed and the reader quit, leaving the socket open and the slot held until the HTTP timeout. The slot now sets up its stream at launch and signals it with the begin result, session.created waits for it, and a reader that stops closes the socket so the session ends with it. * server: name the committed item in input_audio_buffer.committed Per the Realtime spec, item_id is the item made from the committed audio and previous_item_id the one committed before it, null for the first. committed now comes before the transcript that completes that item, and both go out with the item switch under one lock, so a delta cannot land between them. * server: use the GA transcription session shape The session object follows the GA Realtime schema: type transcription, with the input format, transcription model, turn detection and noise reduction under audio.input. The rate stays the model sample rate, which is what the client must send for now. --------- Co-authored-by: Pascal <admin@serveurperso.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