mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-09-27 16:37:29 -05:00
jboothandGeorgi Gerganov
d834d44e64
* Added tiled mul_mat.
For each mul_mat_one_chunk, quants are unpacked into (max) 256x256 tiles of int8,
one routine per quent. Then microkernel computes 16x16 tiles before writing out
256x256 float reults to main memory.
Tests/benches in tests/test-tiled-mulmat.cpp. 3-6x speed improvement
for large matmul, break even at 4096x64 * 64x4096, 80% performance (net
loss) for GEMV. Error rates trivial (order of 1-e04 max, 1-e05 rmse).
* Fixes for ARM/windows builds
* more windows fixes, ggml-cpu.h isn't visible in MSVC for some reason
* unified iqp + tiled on the Q5_K, IQ4_XS set for benchmarking, updated benchmark
* Fixed accidental removal of llama_build_and_test(test-backend-ops.cpp)
* First integration of iqp code
Co-authored-by Bartowski <3266127+bartowski1182@users.noreply.github.com>
* Cleaning up declaration of iq unpacking helpers to align with the bit unpackers
* Removed iqp path
* Fix cross-platform warnings
* Disabling benchmarks unless explicitly enabled
* Fix backend_init for DLL-based builds, add self and bartowski to CODEOWNERS for tiled
* Put benchmarks behind a flag
* kernel fix for AVX2, iq quants
* Fix for asan, leaking memory in test-tiled-mulmat and avoid stack use after return
* guarding env flags with std::call_once
* Simplified repacking for VNNI to a single call per macrotile
* No threadlocals anymore, aligned wdata access
* Doing aligned reads since we ensure alignment with padding in wdata
* Eliminated per-thread gather of Q8_K rows in mul_mat_id, we now gather/repack in a single pass. Repack method now takes pointer array to support both dense/normal and mmid paths. Interface with ggml-cpu.c simplified as a result
* Unified/simplified dispatch and support checks. Put details on wdata needed inside the kernel.h body, simplified interactions with ggml-cpu.c.
* Cleanup includes and whitespace, update src1_repack to return false if we don't need a special repack, so the common case is handled by driver
* Better detection of win32 and additional whitespace fixes
* Gating fuzz tests behind a parameter and some extra prints to try and fix slow CI hosts
* Optimized AVX2 kernel
* Changed interleave format and added ability to interleave in-place after dequant
* Repacks now happen in-place, 16x64 microtiles are independent of each other
* Only repack rows in groups of 16 as they're needed. Save work in low n_rows cases and optimize L1 usage in other cases
* Use long panels for memory-bound regime (M <= 16), reintroduce IQP path for benchmarks
* Fix unused warnings and cleanup. Improved IQ dequantization speed.
* Removed separate process benchmarks
* Revert "Removed separate process benchmarks"
This reverts commit 0688cf43d5.
* AVX2 optimizations and guards for tests on windows
* Removed temp perf harness
* Remove perf-mulmat from build
* Removed IQP path, simplified tests to not use sub processes
* Cleaning up alignment of wdata
* Whitespace fixes and aligning L2 workspace to clean 512kb boundaries
* Update ggml/src/ggml-cpu/tiled/tiled-kernel.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Cleanup merge-duplicated declaration of test-backend-ops target
* Undo accidental line deletion in ggml.c
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.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