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Hrishith Thadicherla
4453b535fd
* llama: preserve original batch order for layer inputs Assisted-by: Codex * tests: cover layer-input order across KV layouts Assisted-by: Codex * tests: exercise layer-input ordering on CUDA devices Assisted-by: Codex * llama: make layer input reordering compatible with tensor split Copy each microbatch tensor from offset zero and restore original row order after synchronization. Extend the layer-input regression to cover tensor split and repeated reads and decodes. Assisted-by: Codex * llama: restore token order for unmasked NextN embeddings Use the original-token mapping for unmasked NextN rows, including when layer-input capture is disabled. Keep masked NextN rows on the logits output mapping and preserve offset-zero tensor copies. Extend the existing regression to cover NextN alone, combined layer capture, and masked outputs with repeated decodes and getters. Validation: all 256 CPU/CUDA/tensor configurations pass. Qwen3.8-27B Q4_K_M MTP completes MT-Bench at concurrency 16 before and after. Assisted-by: Codex * ggml: fix WebGPU reservation and OpenVINO hidden-state capture Reserve WebGPU vector attention scratch across batch sizes and refresh reservations when NextN capture settings change. Preserve requested OpenVINO outputs, dynamic shapes, sequence counts, and current graph bindings. Extend existing WebGPU regression coverage and enable strict allocation checks. Assisted-by: Codex * llama: defer regression test and backend fixes to follow-ups Keep this PR focused on restoring token order for layer inputs and unmasked NextN embeddings. Remove the added regression test, OpenVINO and WebGPU changes, and the separate NextN reservation change. Assisted-by: Codex * llama: keep n_embd declaration in its original position Assisted-by: Codex * llama : pass token count to layer input extraction Assisted-by: Codex * llama : name original batch indices batch_idxs Assisted-by: Codex * llama : name extracted embedding indices embd_batch_idxs Assisted-by: Codex * llama : tag target embedding reordering Assisted-by: Codex * llama : tag extraction and name the index capture flag Assisted-by: Codex
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