LLM inference in C/C++
ggml / ops / maintainer PRs / dev stats / lib llama API / llama-server REST API
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
VLM session with llama cli
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Built-in web UI against llama serve
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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.
| 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 |
- How to build
- Running on Docker
- Build on Android
- Multi-GPU usage
- Performance troubleshooting
- GGML tips & tricks
- XCFramework
- Completions
- Models
- Release process
This fork adds a non-destructive hybrid cache layer to llama-server.
Prepared-prompt KV state is saved to an LRU hot store, optionally backed
by a cold disk store, and restored on exact-match re-use. Unlike the
upstream legacy FIFO cache, hybrid mode preserves earlier entries when
new ones arrive -- old data survives until evicted by LRU pressure.
CLI usage (all flags can also be set via environment variables):
# Basic hybrid cache
llama-server -m model.gguf --cache-mode hybrid -c 8192
# Limit hot RAM to 4 GiB
llama-server -m model.gguf --cache-mode hybrid --cache-ram 4096 -c 8192
# Add cold disk layer for overflow
llama-server -m model.gguf --cache-mode hybrid --cache-cold-path ./cold-cache --cache-cold-max-mib 8192 -c 8192
# Context checkpoints for agentic reuse
llama-server -m model.gguf --cache-mode hybrid --ctx-checkpoints 32 --checkpoint-min-step 8192 -c 16384
# Speculative decoding with DFlash2
llama-server -m model.gguf --cache-mode hybrid --spec-type draft-dflash -c 8192
Hybrid helps with repeated agentic requests, prepared-prompt reuse, or warm-cache exact-repeat reuse. Legacy mode stays the default.
Full flag reference:
| Flag | Description | Default |
|---|---|---|
--cache-mode MODE |
Cache mode: legacy (FIFO, destructive) or hybrid (LRU, non-destructive) |
legacy |
-cram, --cache-ram N |
Max hot cache size in MiB | 8192 |
--cache-cold-path PATH |
Path for cold payload storage (enables cold layer) | disabled |
--cache-cold-max-mib N |
Max cold cache payload size in MiB | -1 (no limit) |
-ctxcp, --ctx-checkpoints N |
Max context checkpoints per slot | 32 |
-cms, --checkpoint-min-step N |
Min spacing between checkpoints in tokens | 8192 |
- 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
- 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