CUDA: fuse shared experts into MMVQ by am17an · Pull Request #29184 · ggml-org/llama.cpp

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cuda llamacpp gpu-optimization inference mixture-of-experts kernel-fusion

Summary

A llama.cpp pull request (#29184) by am17an fuses shared experts into the MMVQ (matrix-matrix vector quantization) CUDA kernel, optimizing inference for mixture-of-experts models on GPUs.

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Cached at: 10/02/26, 08:53 PM

ggml-org/llama.cpp

Source: https://github.com/ggml-org/llama.cpp

llama.cpp

llama

LLM inference in C/C++

License: MIT Release Nightly Server Docker Winget

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

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` VLM session with llama cli Built-in web UI against `llama serve` running Qwen 3.6 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

BackendTarget devices
BLASAll
BLISAll
CANNAscend NPU
CUDANvidia GPU
HIPAMD GPU
HexagonSnapdragon
IBM zDNNIBM Z & LinuxONE
MUSAMoore Threads GPU
MetalApple Silicon
OpenCLAdreno GPU
OpenVINO [In Progress]Intel CPUs, GPUs, and NPUs
RPCAll
SYCLIntel GPU
VirtGPUVirtGPU APIR
VulkanGPU
WebGPUAll
ZenDNNAMD 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
  • 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

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