mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-10-03 03:17:32 -05:00
Georgi Gerganov
631109b34d
* ggml : add `alloc_buffer_n` to buffer type interface Add alloc_buffer_n method to ggml_backend_buffer_type_i interface, with a public API ggml_backend_buft_alloc_buffer_n. - Default implementation in ggml-backend.cpp handles multi-buffer splitting and tensor allocation via ggml_tallocr - Meta buffer type provides custom implementation that creates per-device sub-contexts and delegates to simple buffer types - ggml_backend_alloc_ctx_tensors_from_buft now collects tensors into a list and delegates to the new API - Remove temporary ggml_backend_meta_alloc_ctx_tensors_from_buft - Add NULL alloc_buffer_n to all existing buffer type interfaces (cpu, metal, openvino, hexagon, webgpu, zdnn, virtgpu, repack) Assisted-by: llama.cpp:local pi * cont : fix `cur_buf_size` init after flushing a buffer * ggml : add TODO tag for shared buffer split logic Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * tests : add alloc_buffer_n coverage Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * cont : fix compile warnings * tests : add descriptions for alloc_buffer_n tests Assisted-by: pi:llama.cpp/Qwen3.8-27B * ggml : address review comments on alloc_buffer_n - restore GGML_LOG_ERROR on buffer alloc / tensor init failure in the default impl (name the failing tensor) - check the malloc result and drop the _impl indirection in ggml_backend_alloc_ctx_tensors_from_buft - remove comments that restate the code - fix the TAG_ALLOC_SHARED_BUFFER_SPLIT typo Assisted-by: pi:llama.cpp/Qwen3.8-27B * ggml : add get_alloc_size_n to buffer type interface - Add ggml_backend_buft_get_alloc_size_n public API - Add optional get_alloc_size_n callback to ggml_backend_buffer_type_i - Share tensor->buffer planning between alloc_buffer_n default and get_alloc_size_n default - Replace unchecked realloc with std::vector in alloc_buffer_n default - Make ggml_backend_alloc_ctx_tensors_from_buft_size use the new API - Add test-alloc coverage for get_alloc_size_n Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * cont : report malloc failure
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
|
|
|
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