Georgi Gerganov
073b74034b
* metal : fix mul_mat_id limits and kernel name * metal : fix mul_mat_id with duplicate ids kernel_mul_mm_id_map0 assumed at most one match per token and a fixed capacity of ne21 entries per expert, which produced wrong or dropped output rows and could overflow into the slice of the next expert when the ids contain duplicates. build a compact per-expert list of the (token, slot) entries instead: - pass 1 counts the entries of every expert, thread 0 computes the offsets of the expert slices (tpe now holds [base, count]) - pass 2 writes the entries with one unconditional store per token (conditional stores of divergent lanes are very slow), the dummy value of the tokens without a match lands on a padding slot of the slice that is never read - duplicates are handled by a rare branch - kernel_mul_mm_id reads the base/count of its expert and loops over the entry tiles that do not fit into the grid The ids buffer is sized as ne20*ne21 + ne02 (entries + padding). 883/883 test-backend-ops MUL_MAT_ID tests pass, llama-bench is within run-to-run variance of the previous behavior. Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-MOPD * metal : make mul_mat_id map0 ne02 a function constant use FOR_UNROLL for the id loops of kernel_mul_mm_id_map0 and turn ne02 into the function constant FC_mul_mm_id_map0_ne02 (FC_MUL_MM + 7), so that the threadgroup offsets of cnt/bas/sids and the prefix-sum bound fold at compile time. the pipeline name now encodes ne02 (same convention as the ssm_conv nc/silu constants) and the map0 pipeline is compiled with the constant value set. ne02 is no longer part of ggml_metal_kargs_mul_mm_id_map0 - the dispatch grid still comes from the tensor ne[2] on the host. no measurable perf change: 883/883 test-backend-ops MUL_MAT_ID tests pass, op-level and llama-bench results are within run variance. Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-MOPD * cont : clean-up + TODO * metal : dispatch mul_mat_id entry tiles from the prepass follow the approach of the vulkan implementation (#29998): instead of launching a grid over experts x ne21 entry tiles and letting most of the threadgroups early exit, kernel_mul_mm_id_map0 now also emits a row map - the total tile count plus one (expert, first entry, valid rows) descriptor per 32-entry tile that has work. kernel_mul_mm_id is launched with a static upper bound on the number of tiles (full tiles over all entries plus at most one partial tile per expert), reads its descriptor and processes a single tile - the tail early exits. This removes the round loop that was needed to cover duplicate ids past the grid (register pressure: th_max 768 -> back to the plain mul_mm level) and duplicates are now handled implicitly, since they only produce more descriptors within the bound. The grid shrinks by ~8-16x on typical shapes (e.g. 128 experts, 8 used, 512 tokens: 128 tiles instead of 2048 entry tiles, of which 94% only launched to exit) and ne21 is no longer needed by the mm kernel kargs. 883/883 test-backend-ops MUL_MAT_ID tests pass. llama-bench (Qwen3.6-35B-A3B Q8_0) is now faster than master on all pp sizes (pp2048/4096/8192 +2%, pp512 +0.5%), tg32 unchanged. Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-MOPD * cont : add smem assert
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