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Daniel Han
ad4fa3fa7b
Adds the per-layer embedding: a custom I32 graph input hashes each token with its ngram_size-1 predecessors host-side and the result is a plain row gather over the shared table, the same shape gemma3n's per-layer embedding uses. The hash has to run on the host because the splitmix64-derived multipliers reach 2^45, so the products need 64-bit integers and an xor, neither of which ggml has. Predecessors that fall outside the ubatch come from a small per-sequence history on the model, mirroring the per-request ngram_context the reference carries. It is only trusted when contiguous with the incoming position, so a fresh prompt or a rewound cache falls back to EOS padding rather than hashing against stale tokens. The depthwise conv is written out as a sum of shifted, per-channel-scaled copies rather than through ggml_conv_1d_dw, which carries a correctness warning upstream. Verified two ways. The row indices match a transcription of the reference's tensor formulation exactly, 1024 of 1024 rows, including sequences with EOS tokens sprinkled through them to exercise the segment reset. Separately, with PLE placed on layer 0 so its input is just the token embedding, ple_embd and ple_gated_value match a PyTorch computation from the same checkpoint to every printed digit. End to end over 1023 scored positions the port sits the same distance from vLLM with PLE as without it, 6.3 points of top-1 against 6.0, so PLE costs no accuracy relative to the rest of the model. That common offset is vLLM's bf16 activations, which cannot be removed: its QSA kernel refuses float32. Two bugs found along the way, both caught by the row-index check. The history was read and updated in the same pass, so a token early in a ubatch could pick up an earlier token of that same ubatch as prior context; it is now snapshotted first. And an EOS token was cutting its own context, where the reference takes the last EOS strictly before the position, so a boundary only hides tokens from the positions after it. Known gap: the conv carries no state across ubatches, so it is exact only for a prefill that starts at position 0. Chunked prefill and decode need the conv state wired into the recurrent memory, and the conv branch itself is still numerically unverified because the fixture zeroes its weights.
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