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Hongqiang Wang
c841aeeb8b
* opencl: default the Adreno xmem F16xF32 GEMM on for X2E kernel_mul_mm_f16_f32_l4_lm is the slowest matmul this backend has on Adreno: on the X2-90 it runs the gpt-oss-20b attention projections at roughly a quarter of what the tuned dense q4_0 GEMM reaches on the same device. That matters for any model whose non-expert weights stay f16 -- the stock gpt-oss-20b release is exactly that, and its prefill spends 40.8% of GPU time in that one kernel. The xmem route already existed but was left opt-in, so nobody hit it. Worth about 25% prefill on gpt-oss-20b on an Adreno X2-90. Gated to X2E: the Adreno 840 measures neutral. Decode is untouched -- the dispatch gate needs N >= 16. It is worth nothing on the q8attn variant, whose attention weights already take the dp4a dense GEMM. The env var was presence-tested before, so =0 previously enabled it; it is now atoi()'d. MUL_MAT 963 OK / 0 FAIL on both arms. * opencl: bypass the tiled f32 GEMM on the Adreno A7X The A7X (E031.41) compiler executes kernel_mul_mm_f32_f32_l4_lm at roughly a tenth of what the same silicon reaches in its own f16 and q4_K kernels. It allocates 488 B/WI of private memory against 304 for the same source on the following generation, i.e. the older register allocator spills in the K-loop. Models with per-layer F32 projection pairs kept F32 by quantization policy land on this kernel twice per layer, and it dominates their prefill on that part. Route batched f32xf32 (ne11 > 8) around the tiled path on the A7X and let it fall through to the per-row f32 kernel, which that compiler handles fine; small batches keep the tiled path. Weights stay GPU-resident, so decode placement is untouched -- declining the op in supports_op instead was measured first and rejected, because the per-layer CPU round-trips cost more decode than the prefill it gained. Worth about 9% prefill on gemma-3n-E4B on an Adreno 740, with MUL_MAT counts identical on and off. No other generation is affected. Override with GGML_OPENCL_A7X_F32_LM_BYPASS=0. * opencl: enable xmem GEMM for adreno by default --------- Co-authored-by: Li He <lih@qti.qualcomm.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 [In Progress] | 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