mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-09-24 13:37:01 +02:00
Georgi Gerganov
5b59b83f4e
* metal : add top-k MoE fusion Adds a Metal fusion for SOFT_MAX + ARGSORT + GET_ROWS with optional routing-weight normalization and scale, matching the top-k MoE fusion available in the CUDA and Vulkan backends. The fused kernel writes the selected expert ids and routing weights directly, eliding the separate softmax, argsort, get-rows, sum-rows, clamp, div and scale kernels. Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : add MoE weighted reduction fusion Fuses MUL(experts, weights) plus the expert VIEW/ADD chain into one kernel that computes the weighted sum directly. The graph_optimize hook keeps the expert and weight buffers alive until the fused output so the allocator cannot reuse them while the kernel is still reading them. Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * tests : expose MoE weighted reduction in fusion baseline Use 2 experts per token in the generated MoE test models so the Metal MoE weighted reduction fusion (MUL + ADD) is exercised by test-fusion. Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : fuse RMS_NORM + SCALE Adds NORM/RMS_NORM + SCALE fusion to the Metal backend by reusing the norm+mul kernel with a scalar scale flag. Adds test coverage for both NORM+SCALE and RMS_NORM+SCALE and regenerates the fusion baseline. Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : use function constant for RMS_NORM + SCALE Replaces the runtime use_scale karg with a Metal function constant. The norm+mul kernel is compiled with FC_norm_use_scale=false for MUL fusion and FC_norm_use_scale=true for SCALE fusion, so the fused kernel has no runtime branch. Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : use function constant for top-k MoE with_norm Replaces the runtime with_norm karg with a Metal function constant. The top-k MoE kernel is compiled separately for the normalized and non-normalized routing variants, removing the runtime branch. Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : rename moe_weighted_reduction suffix to moe_reduce Shortens the MoE weighted-reduction fusion identifiers, kernel, pipeline, matcher, args struct, and test op name from moe_weighted_reduction to moe_reduce. Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : add MUL_MAT + UNARY and MUL_MAT + ADD + UNARY fusion Adds dense mat-vec activation fusion for sigmoid/silu and bias+softplus. The mat-vec kernels apply the activation/bias epilogue via function constants, avoiding the separate unary/add passes. Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : revert MUL_MAT + UNARY and MUL_MAT + ADD + UNARY fusion The mat-vec activation fusion regressed decode throughput on Qwen3.6-35B-A3B by ~8% (tg32 81.5 vs 88.5 t/s). The regression is caused by loss of concurrency: the standalone unary kernels previously overlapped with other mat-vec work, while fusing the activation into the mat-vec kernel serializes it on the critical path. Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : add SSM_CONV + UNARY (silu) fusion The SSM_CONV kernels apply silu directly via a function constant, eliding the separate unary pass. Regenerates the fusion baseline. Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : address fusion review comments - Fix declaration/table alignment - Rename top-k MoE kargs fields to val_clamp / val_scale - Move moe-reduce alloc-deps handling into a general fusion helper - Remove the public moe-reduce matcher API Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : fix unused parameter in top-k MoE fusion check Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : guard SSM_CONV fusion lookup behind use_fusion Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : track all fused outputs in graph reorder Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : keep top-k MoE logits alive until fused output Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : refactor alloc deps to pattern-driven approach Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : check fused kernel destination in concurrency tracking Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * meta : forward graph_optimize to underlying backends Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : use vector for fusion table Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * meta : keep graph_optimize unimplemented Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * parallel : fix non-deterministic prompt selection Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * parallel : support dummy models and add global logits run hash Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : sync cross-device copies with destination completion event Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : avoid const_cast in fusion alloc deps Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : skip fusions with aliased sources Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : hide fusion pattern definition Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : use vector fusion op sequences Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : drop redundant struct keywords Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : add alloc deps comment separator Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : generalize fusion output memory ranges Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : rename fusion out_offsets to outs Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : avoid dst vector in memory range check Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : optimize fusion matching and multi-output handling - use pointer arithmetic for fusion info count lookup - avoid heap allocations in top-k MoE and MoE reduce pattern matchers - use fusion outs for multi-output subgraph checks Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * Revert "parallel : support dummy models and add global logits run hash" This reverts commit 57c7caf941c1b43c270fd5009c9f175063522e96. * fusion : update MTL.csv * metal : unroll constant loops in top-k MoE kernel Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : use function constants for top-k MoE n_expert and top_k Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : rename fusion kargs to scale and clamp Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * metal : use function constants for moe_reduce and ssm_conv Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp * fusion : update MTL.csv
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