hey-gmandOliver Simons
1167d3f42c
* CUDA: fix CCCL version guard breaking on major version rollover
The guard compared the major and minor components independently:
CCCL_MAJOR_VERSION >= 3 && CCCL_MINOR_VERSION >= 1
Minor resets to 0 whenever a new major series is cut, so on CCCL 4.x
this evaluates as 4 >= 3 && 0 >= 1, i.e. false. STRIDED_ITERATOR_AVAILABLE
stops being defined and argsort silently falls back to the
init_offsets path. Nothing warns and the build still succeeds, so the
regression is a quiet performance loss rather than a compile error.
CCCL already exposes the version as a single packed integer in
MMMmmmpp form, which is what its own version header uses:
CCCL_VERSION = MAJOR * 1000000 + MINOR * 1000 + PATCH
so 3.4.3 is 3004003 and ">= 3.1" is a plain ">= 3001000". One
comparison, with no component arithmetic left to get wrong.
Checked against a hand-written "version >= 3.1" reference over 2.9.9,
3.0.0, 3.1.0, 3.1.99, 3.2.0, 3.4.3, 3.9.9, 3.99.99, 4.0.0, 4.2.7 and
5.0.0: no divergences. The old guard disagreed at 4.0.0 and 5.0.0.
Verified on RTX 4070 (sm_89), CUDA 13.4, CCCL 3.4.3:
- cmake --build build --config Release: exit 0
- test-backend-ops test -o ARGSORT -b CUDA0: 98/98 passed, CUDA0 OK
Note that a passing regression test does not on its own prove the guard
is still taken, since the fallback path passes too. Preprocessing the
real translation unit confirms the strided-iterator branch is the one
compiled in: counting_iterator is present, init_offsets is not.
Signed-off-by: Heitor <heitorgm@outlook.com>
* Update ggml/src/ggml-cuda/argsort.cu
* Apply suggestion from @ORippler
---------
Signed-off-by: Heitor <heitorgm@outlook.com>
Co-authored-by: Oliver Simons <osimons@nvidia.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:
# 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