csantiago78andClaude Fable 5
7391a178e6
Measured on Qwen3.8-Flash-Next UD-Q4_K_XL (512 experts, 10 routed, 28 expert layers pinned to host RAM by -ot): expert routing has strong temporal locality (LRU-64 ~67% hit rate over a mixed workload) even though the long-run expert distribution is near-uniform, so a per-layer LRU cache of expert slices in VRAM removes most of the per-token host-RAM streaming that bounds decode. Mechanism (no custom CUDA kernels): - companion tensors [ne0, ne1, K+1] per cached layer in the device buffer of that layer's router; slot K stays all-zero (dummy) - I32 id->slot tables, one device copy (get_rows remaps ids for a second mul_mat_id chain over the cache) and one host copy (src[3] of the CPU mul_mat_id, which skips cached ids and zeroes their dst rows) - the two down outputs are summed; the split is exact by construction - decode-only (n_tokens == 1); batch/prefill builds the stock graph - throttled async uploads: evictions are published at a decode-boundary sync point, slices are copied by a worker thread, and the new mapping is only published after the upload completed, so a running graph never reads a torn slot Enable with LLAMA_MOE_CACHE_SLOTS=<K> (+ LLAMA_MOE_CACHE_INSERTS, _DEBUG). Inert without the env var. Measured decode, this box (2x3090, 1 RAM channel/socket, with numactl --interleave=all): 15.5 -> 19-20 tok/s warm at K=48-64. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A7KRyLzuBiGfVn3bdczjka
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