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https://github.com/ggml-org/llama.cpp.git
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cqderek
f027c4f1b0
Extend the HTP backend's F16 unary op coverage to include ABS on top
of the existing NORM/RMS_NORM/L2_NORM/SCALE/CLAMP/SQR/SQRT set.
- Add hvx_abs_f16_{aa,au,ua,uu} + dispatcher in hvx-arith.h, mirroring
the sqr_f16 kernel structure and using the existing hvx_vec_abs_f16()
sign-bit-clear helper
- Add abs_f16() row-wise dispatch and DEFINE_UNARY_TASK_F16(unary_abs, ...)
in unary-ops.c, wired into execute_op_unary()'s op_type/task_func
switches
- Register HTP_OP_UNARY_ABS in htp_op_is_unary() (unary-ops.h) so that
ggml_hexagon_precompute_unary_params() fills kernel_params (n_threads,
VTCM layout) for ABS nodes -- required for the F16 path to function
- Narrow the F16 GGML_OP_UNARY gate in ggml_hexagon_supported_unary()
(ggml-hexagon.cpp) to allow GGML_UNARY_OP_ABS specifically, instead of
rejecting all GGML_OP_UNARY ops for F16
- Merge the separate execute_op_unary_f32()/execute_op_unary_f16()
functions into a single execute_op_unary(), branching on an is_f16
flag for the parts that actually differ by type (elem_size, the
early F16 op-support check, and which task_func table to use) while
keeping the F32-only tiled/RMS_NORM_MUL paths intact -- per review
feedback to avoid duplicating the shared VTCM/DMA plumbing
Verified on-device (QRD8850, Hexagon v81) via test-backend-ops -o ABS:
8/8 passing (F16 + F32, HTP0, no CPU fallback). Regression-checked
SQR/CLAMP/SQRT (F16+F32) and NORM/RMS_NORM/L2_NORM/SCALE (F32; their F16
paths have no CPU reference kernel in test-backend-ops and cannot be
correctness-tested there independent of this change).
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 [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
- 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