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* ci: prepare for amd rocm ci Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * ci: fix editorconfig-checker Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * ci: fix device not recognised Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * ci: rename gpu-amd to gpu-hip Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * ci: gpu-hip to gpu-rocm haha Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * CUDA: allow integrated-GPU host output buffer in debug assert On integrated GPUs (APUs), the scheduler can legitimately place a graph node's output on the host-visible buffer, which ggml_cuda_compute_forward already handles. The debug assert in ggml_cuda_graph_evaluate_and_capture required every node output to be on the device buffer, so a debug build aborts on such a node (e.g. attn_residual ADD -> ROCm_Host on RDNA3.5). The source-tensor assert directly below already permits this via the integrated + cuda_host exception; apply the same exception to the node's own output buffer. Debug-only; no effect on release/compute. Fixes test-recurrent-state-rollback on gfx1151 (Strix Halo). * ci: enable unified memory for ROCm gfx1151 job Work around a coherence issue on integrated RDNA3.5 (gfx1151) where GPU kernels reading mmap-loaded weights can return incorrect output, which makes test-llama-archs (and real inference) intermittently wrong. GGML_CUDA_ENABLE_UNIFIED_MEMORY=1 uses managed memory, which restores coherence. Remove once the underlying ROCm/HIP issue is fixed. * test-llama-archs: skip jamba on HIP backend jamba produces incorrect output (~0.55 NMSE vs CPU) on the HIP backend on RDNA3.5 (gfx1151); the SSM kernels need separate investigation. Skip it for now, matching the existing per-backend carve-outs (WebGPU), so the ROCm CI can run the test for the remaining architectures. * ci: use HIP_LAUNCH_BLOCKING for ROCm gfx1151 job The gfx1151 ROCm CI job produced incorrect inference output (qwen3 perplexity ~88 vs ~9.4) due to an async-execution correctness issue in the HIP path. Serializing kernel launches with HIP_LAUNCH_BLOCKING=1 restores correctness. This replaces the earlier GGML_CUDA_ENABLE_UNIFIED_MEMORY workaround, which did not fix batched inference. * test-backend-sampler: skip top-k subtests on HIP backend The ROCm backend does not support the TOP_K/ARGSORT op at vocab scale (no CUB; bitonic argsort is capped at ncols <= 1024), so top-k/top-p backend samplers cannot be offloaded. The penalties, set_sampler, mixed, and top_p subtests assert that offload happened, so they fail on HIP. Skip them until TOP_K is supported on the ROCm backend. * Update tests/test-backend-sampler.cpp Co-authored-by: Aaron Teo <taronaeo@gmail.com> * Update tests/test-backend-sampler.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> Co-authored-by: Aaron Teo <aaron.teo1@ibm.com> Co-authored-by: Jim Wu <ywu@xilinx.com> Co-authored-by: Aaron Teo <taronaeo@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
llama.cpp
LLM inference in C/C++
manifesto / ggml / ops / maintainer PRs / dev branches / compile times / 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
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 - stb-image - Single-header image format decoder, used by multimodal subsystem - Public domain
- nlohmann/json - Single-header JSON library, used by various tools/examples - MIT License
- miniaudio.h - Single-header audio format decoder, used by multimodal subsystem - Public domain
- subprocess.h - Single-header process launching solution for C and C++ - Public domain