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https://github.com/LostRuins/koboldcpp.git
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Max Krasnyansky
eafe15a5e3
* hex-row-split: add support for multi-device row spliting Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com> * hex-mdev: add work splitting to fused kernels * hex-mdev: use mdev_ prefix for all multi-device state * hex-mdev: make device configuration more expressive to support device groups * hex-mdev: fix mdev session init * hex-mdev: fused nx (2x,3x) matmuls must update row counts for each w/o * hex-mdev: fix MUL_MAT work partitioning bugs introduced by mdev * hex-cont: fix crashes with new tests due to wrong striding * hex-mdev: move fences after l2flushes * hex-cont: fix work splitting for mnpu -- align chunks to cachelines * hex-mdev: fix CPY tests with multi-dev * hex-mmid: fix work partitioning with mnpu * hex-mm: fix test failures with mdev * hex-binary: fix work partitioning for mdev * hex-argsort: fix mdev partitioning * hex-mdev: fix work partitioning and general updates for all simple ops * hex-fa: fix mdev work splitting issues * hex-mdev: fixing more failing ops test * hex-mdev: update the rest of the ops * hex-mdev: refactor all mdev splitting logic to be contained within if (mdev_count > 1) {...} * hex-mdev: fix macros * hex-mdev: simplify session flush logic * hex-sync: fix recursion in session flush * hex-mdev: factor out fence buffer and allocator * hex-fence: make fence allocation more robust with reserved slots for mdev * hex-mdev: keep all mdev state in htp_mdev_group * hex-mdev: further cleanup mdev group handling at the host * hex-mdev: update group idx in the opbatch before serializing * hex-batch: remove separate op_pending and use batch_req/rsp_seq * hex-async: workaround another missing tensor_init in ggml-meta * hex-fence: cleanup and robustify fences and error handling in multi-device scenarios * hex-ar: improve ALLREDUCE error handling * hex-async: robust error handling for op_cpy_fence * hex-async: use seq0 from allreduce context to allocate fence_seq * hex-mdev: fix remaining issues with fence and barrier clearing in CPY_FENCE * hex-misc: realign macros and fix misplaces trace events * hex-misc: align macros * hex-mdev: fix unclone buffer re-entrancy * hex-glu: fix mdev partitioning logic * hex-mdev: make buffer uncloning/cleanup work with tensor-split scenarios * hex-mdev: tighten up the can_split check in act-ops * hex-mdev: factor out common bits of the partitioning logic * hex-mm: minor realignment of the macros * hex-bufs: fix incorrectly placed assert for MAX_BUFS * hex-pad: tighten up gating checks for PAD * hex-kparams: make sure all kernels properly use kparams->n_threads * hex-docs: update user and developer docs with new features and detailed guide for ops development * hex-scripts: update run script to properly parse dev groups * hex-misc: formatting * hex-sess: minor cleanup for session init * hex-ar: fix vtcm size calc in allreduce kparams * hex-scripts: fix flake8 warnings * hex-rope: update ROPE to support mdev work split * hex-ops: remove redunant checks and minor reformat * hex-dev-guide: update dev-guide to avoid redundant null checks * hex-async: improve event_wait, event_sync and fence implementations * hex-async: remove synchronous flush from event_sync * hex-async: symplify fence recovery protocol and make sync more robust * hex-async: futher simplify error recovery for fences * hex-err: return status instead of just -1 * hex-async: print all seq nums in hex * hex-async: make sure fences flush dirty ranges * hex-async: add dirty ranges merging to reduce fence flushes * hex-async: properly sync before freeing the event * hex-async: make sure fence owner session is not overriden * hex-async: more fence write order more robust * hex-async: make sure not to fuse ALLREDUCE+ADD if their dsts overlap * hex-fusion: cleanup redundant checks --------- Co-authored-by: Alexander Lu <alexlu@qti.qualcomm.com>
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