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* metal: implement F16 Lightning Indexer - Implement GGML_OP_LIGHTNING_INDEXER for 128-dimensional, 64-head inputs with F32 queries and weights plus F16 keys and masks. - Add tiled and tail kernels and test KV lengths around 8- and 64-element boundaries. llama-bench (--mmap 1, -fa 1, -p 512, -n 128; d=0/10k/20k/30k): Before: - pp512: 153.73 ± 0.87 t/s - tg128: 8.91 ± 0.04 t/s - pp512 @ d10000: 73.90 ± 0.39 t/s - tg128 @ d10000: 8.66 ± 0.03 t/s - pp512 @ d20000: 45.83 ± 0.18 t/s - tg128 @ d20000: 8.26 ± 0.03 t/s - pp512 @ d30000: 33.40 ± 0.21 t/s - tg128 @ d30000: 7.94 ± 0.01 t/s After: - pp512: 155.19 ± 0.91 t/s - tg128: 8.95 ± 0.04 t/s - pp512 @ d10000: 86.95 ± 0.69 t/s - tg128 @ d10000: 9.00 ± 0.05 t/s - pp512 @ d20000: 62.01 ± 0.45 t/s - tg128 @ d20000: 8.68 ± 0.04 t/s - pp512 @ d30000: 49.18 ± 0.33 t/s - tg128 @ d30000: 8.60 ± 0.02 t/s Assisted-by: Codex * metal: stage Lightning Indexer K tiles - Stage and dequantize K in F16 threadgroup memory before simdgroup matrix loads. - Zero-fill partial tiles and guard stores so all KV segments use the same numerical path. - Support F32, F16, BF16, Q4_0, Q4_1, Q5_0, Q5_1, and Q8_0 K caches. llama-bench (--mmap 1, -fa on, -p 512, -n 128; d=0/10k/20k): - pp512: 160.38 +/- 1.01 t/s - tg128: 9.08 +/- 0.03 t/s - pp512 @ d10000: 88.37 +/- 0.46 t/s - tg128 @ d10000: 9.07 +/- 0.04 t/s - pp512 @ d20000: 62.53 +/- 0.46 t/s - tg128 @ d20000: 8.84 +/- 0.03 t/s Assisted-by: Codex * dedup Lightning Indexer constants, fix flaky test * cont : fix whitespace --------- Co-authored-by: forforever73 <690105611@qq.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> |
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| convert_hf_to_gguf.py | ||
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llama.cpp
LLM inference in C/C++
manifesto / ggml / ops / maintainer PRs / dev branches / compile times / 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 [In Progress] | 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
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 - stb-image - Single-header image format decoder, used by multimodal subsystem - Public domain
- nlohmann/json - Single-header JSON library, used by various tools/examples - MIT License
- miniaudio.h - Single-header audio format decoder, used by multimodal subsystem - Public domain
- subprocess.h - Single-header process launching solution for C and C++ - Public domain