Project Title

llama.cpp — High-Performance LLM Inference in C/C++

Overview

llama.cpp is an open-source project aimed at providing low-latency, high-performance inference for large language models (LLMs) using C/C++. It stands out for its plain C/C++ implementation without dependencies, support for various hardware architectures, and advanced quantization techniques for faster inference and reduced memory usage.

Key Features

Plain C/C++ implementation with no external dependencies
Optimized for Apple silicon with ARM NEON, Accelerate, and Metal frameworks
Supports AVX, AVX2, AVX512, and AMX for x86 architectures
Offers 1.5-bit to 8-bit integer quantization for efficient inference
Custom CUDA kernels for NVIDIA GPUs, with support for AMD GPUs via HIP and Moore Threads GPUs via MUSA
Includes Vulkan and SYCL backend support for additional hardware acceleration
CPU+GPU hybrid inference for models larger than VRAM capacity

Use Cases

Researchers and developers needing to deploy LLMs on various hardware for performance-critical applications
Enterprises looking to integrate LLMs into their products with minimal setup and high efficiency
Educational institutions using LLMs for teaching and research purposes, benefiting from the project's flexibility and performance

Advantages

State-of-the-art performance across a wide range of hardware, including local and cloud environments
Minimal setup and maintenance due to the lack of external dependencies
Advanced quantization options for reduced memory footprint and faster inference times
Active community and regular updates, ensuring ongoing support and improvements

Limitations / Considerations

The project's performance may vary depending on the specific hardware and model being used
Custom CUDA kernels and backend support may require additional setup and configuration for non-standard hardware
The project is continuously evolving, which might introduce breaking changes in API and functionality

Hugging Face Transformers: A library of pre-trained models for Natural Language Processing, differing in that it offers a higher-level API and broader model support.
OpenNMT: An open-source machine learning framework for neural machine translation, differing in its focus on sequence-to-sequence models and training capabilities.
Rust BERT: A Rust implementation of BERT models, differing in the programming language used and potentially offering different performance characteristics.

Basic Information

GitHub: https://github.com/ggml-org/llama.cpp
Stars: 86,046
License: MIT
Last Commit: 2025-09-04

📊 Project Information

Project Name: llama.cpp
GitHub URL: https://github.com/ggml-org/llama.cpp
Programming Language: C++
⭐ Stars: 86,046
🍴 Forks: 12,933
📅 Created: 2023-03-10
🔄 Last Updated: 2025-09-04

🏷️ Project Topics

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📚 Documentation

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Manifesto

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llama.cpp

Project Description