vulkan: add POOL_1D op (#25431)

vulkan : add pool1d push constants and pipeline field

Declared data structures needed for POOL1D OP, which are the vk_op_pool1d_push_constants struct and pipeline_pool1d_f32 field.

vulkan : add pool1d compute shader

Added pool1d.comp for Vulkan backend mirroring the existing pool2d shader.

vulkan : add full GGML_OP_POOL_1D support

Added pipeline creation and op dispatch for 1D pooling in the Vulkan backend.

vulkan : fix pool1d shader logic

Registered pool1d_f32 in vulkan-shaders-gen.cpp and fixed tensor dimension indices and avg pool scale.

vulkan : fix pool1d end boundary crash and expand test coverage

Fixed an issue where the shader crashed when the end boundary was negative when k0 < p0. Also, added more test cases related to this fix.

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

VLM session with llama cli

Built-in web UI against llama serve

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

Contributing

Contributors can open PRs
Collaborators will be invited based on contributions
Maintainers can push to branches in the llama.cpp repo and merge PRs into the master branch
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