Oliver SimonsandSigbjørn Skjæret
68d20b884c
* WIP OCP FP8 E4M3 support * Fix python type-checker * Materialize input_scales also for FP8 when converting * Fix conversion of https://huggingface.co/RedHatAI/Qwen3.8-27B-NVFP4 1. Per row FP8 wrongly fell to NVFP4 conversion, as NVFP4 checked on dims alone. Also check on dtype for FP8 2. Need to reshape FP8 QKV projections scales in the same way that weights are reshaped * Allow remaining FP8 tensors to be dequantized during conversion * Allow tensor-strategy for FP8-only compressed-tensor ckpts * Fix and test gguf-py behavior for FP8_E4M3 * Disallow QKV-fusion for FP8 temporarily * Fix conversion of fused qkvz & FP8 for Qwen3-Next * Avoid fusing FP8 for --fuse-gate-up-exps as we may have different ws * Emit warnings when not fusing FP8 during conversion * More FP8 conversion fixes * Add supported for strided copies to/from FP8 in CPU * remove unneeded scratch buffer from FP8 cpu cpy/dup path * add explanatory comments and TODOs * Add dedicated encoder and decoder tests for FP8 * Whitelist ggml_op_get_rows in metal instead of blacklisting required to fix failing CI, and white-listing should be more future-proof than adding FP8 to the black-list https://github.com/ggml-org/llama.cpp/actions/runs/38433916498/job/115422880633?pr=27512 * Fix more conversion bugs * Defer weight materialization during conversion * Blacklist fp8 in metal backend instead of white-list * Adjust indents * Fix lint + execute tests * Add fast CPU kernel for F8_E4M3 * Conversion fixes * Fix remaining gaps * Enable tied weights with shared scales * Make mandatory-F32 rules apply to FP8 during conversion * Fix python type checks https://github.com/ggml-org/llama.cpp/actions/runs/39765670052/job/119780379595?pr=27512 * Fix kaleidai unit-test failures https://github.com/ggml-org/llama.cpp/actions/runs/39765670125/job/119780383150 * Relax numeric precision constraints for pre-check * Leave fast-FP8 CPU on RISC-V for follow-up We already exclude arm from the fast-path * Allow output scale tensors to be located in any gguf shard * Move test_conversion_fp8.py test execution to behavior-check.yaml * Apply batched suggestions from code review Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * Move mixed compressed-tensor logic into dequant_fp8 + add unit-tests for it * Install all requirements also on Windows for behavior-check Fixes https://github.com/ggml-org/llama.cpp/actions/runs/40535852590/job/122300758223?pr=27512 * Unmap lazy tensors before clean-up in Windows Should fix https://github.com/ggml-org/llama.cpp/actions/runs/40539797824/job/122314782459?pr=27512 * LF line endings https://github.com/ggml-org/llama.cpp/actions/runs/40539798168/job/122314791118?pr=27512 * Extract FP8 tests from llama-arch to its own test-fixture See https://github.com/ggml-org/llama.cpp/pull/27512#discussion_r4400628221 * Update gguf-py/gguf/scripts/gguf_convert_endian.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
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:
# curl
curl -LsSf https://llama.app/install.sh | sh
# powershell
irm https://llama.app/install.ps1 | iex
- 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