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https://github.com/ggml-org/llama.cpp.git
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Matt Corallo
83dd71f869
It turns out Intel doesn't particularly like loading F32s one at a time and we already have the _2aliagned load logic in mul_mat_vec, so here we use it. While we do already check all the requirements to load elements 4 at a time across [B]F16 and F32, it turns out [B]F16 loading 4 at a time is sometimes slower on very specific shapes on Intel BMG. Loading 4 at a time is a bit faster on F32, but its not material and I assume might be slower on other platforms. Note that we also need to validate `a_offset` is 2-aligned in `mul_mat_vec.comp`, which was missing in the original 2-way-load patch. Some selected speedups from `test-backend-ops perf` on a B60. MUL_MAT(type_a=f32,type_b=f32,m=4096,n=1,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0): 1704 runs - 767.17 us/run - 117.44 MFLOP/run - 153.08 GFLOPS MUL_MAT(type_a=f32,type_b=f32,m=4096,n=1,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0): 2556 runs - 529.81 us/run - 117.44 MFLOP/run - 221.66 GFLOPS MUL_MAT(type_a=f32,type_b=f32,m=4096,n=2,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0): 1704 runs - 727.13 us/run - 234.88 MFLOP/run - 323.03 GFLOPS MUL_MAT(type_a=f32,type_b=f32,m=4096,n=2,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0): 2130 runs - 528.84 us/run - 234.88 MFLOP/run - 444.15 GFLOPS MUL_MAT(type_a=f32,type_b=f32,m=4096,n=3,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0): 1704 runs - 702.19 us/run - 352.32 MFLOP/run - 501.74 GFLOPS MUL_MAT(type_a=f32,type_b=f32,m=4096,n=3,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0): 1988 runs - 532.14 us/run - 352.32 MFLOP/run - 662.08 GFLOPS MUL_MAT(type_a=f32,type_b=f32,m=4096,n=4,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0): 1278 runs - 919.50 us/run - 469.76 MFLOP/run - 510.89 GFLOPS MUL_MAT(type_a=f32,type_b=f32,m=4096,n=4,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0): 1917 runs - 543.69 us/run - 469.76 MFLOP/run - 864.03 GFLOPS MUL_MAT(type_a=f32,type_b=f32,m=4096,n=5,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0): 1197 runs - 892.12 us/run - 587.20 MFLOP/run - 658.21 GFLOPS MUL_MAT(type_a=f32,type_b=f32,m=4096,n=5,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0): 1881 runs - 575.17 us/run - 587.20 MFLOP/run - 1.02 TFLOPS MUL_MAT(type_a=f32,type_b=f32,m=4096,n=8,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0): 1498 runs - 716.40 us/run - 939.52 MFLOP/run - 1.31 TFLOPS MUL_MAT(type_a=f32,type_b=f32,m=4096,n=8,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0): 1819 runs - 576.36 us/run - 939.52 MFLOP/run - 1.63 TFLOPS MUL_MAT(type_a=f32,type_b=f32,m=4096,n=512,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0): 134 runs - 7467.09 us/run - 60.13 GFLOP/run - 8.05 TFLOPS MUL_MAT(type_a=f32,type_b=f32,m=4096,n=512,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1,src_overlap=0): 134 runs - 7478.12 us/run - 60.13 GFLOP/run - 8.04 TFLOPS
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