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https://github.com/ggml-org/llama.cpp.git
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Kevin Hopper
7a333e7240
* vulkan: add DeepSeek-V4 hyper-connection fused ops (DSV4_HC_COMB/PRE/POST) CUDA has these ops from the DeepSeek-V4 merge and Metal gained them in PR 26459. Vulkan was the last major backend running the unfused primitive chain. On DeepSeek-V4-Flash the unfused Sinkhorn comb chain alone takes about 32% of decode op time on gfx1151 (Strix Halo), spread over roughly 16k dispatches per token. dsv4_hc_comb runs the full 20-iteration Sinkhorn in registers. A token's 4x4 comb matrix lives in 16 consecutive subgroup lanes, with idst in bits 0-1 and isrc in bits 2-3 to match the CPU reference layout, so subgroupShuffleXor by 1|2 reduces rows and by 4|8 reduces columns. One dispatch replaces about 137 strictly ordered node executions per site. The shuffle masks never cross a 16-lane boundary, so a subgroup of size 64 packs 4 independent tokens. dsv4_hc_pre and dsv4_hc_post handle the elementwise stream collapse and fan-out, with per-token coefficients staged in shared memory. GGML_VK_DISABLE_DSV4_HC disables all three ops. The _COMB, _PRE and _POST variants gate each op independently so a single kernel can be bisected against the unfused graph. Adds eval cases at the production n_iter=20 across batch sizes that cross subgroup and workgroup boundaries. * vulkan: dsv4 hc review fixes Drop the per-op env-var disables and device flags, the stride divisibility check (ggml guarantees it) and the workgroup-count fallback in supports_op. Trim the comb shader comments to the lane layout. --------- Co-authored-by: Kevin Hopper <no-reply@maestro.press>
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