llama.cpp is a high-performance C/C++ inference engine for large language models (LLMs) and vision-language models (VLMs). It loads models in the GGUF format and runs them on a wide range of hardware, including CPU, CUDA, Metal, Vulkan, SYCL, OpenCL, Hexagon DSP, and more. The project provides a core library (`llama.cpp` and `ggml`), a CLI client (`llama-cli`), an OpenAI-compatible HTTP server (`llama-server`), a Svelte-based web UI, and a suite of tools for quantization, perplexity evaluation, and model conversion. It supports dozens of model architectures (LLaMA, Mistral, Qwen, Gemma, DeepSeek, etc.) and advanced features like speculative decoding, grammar-based sampling, flash attention, and LoRA adapters. The architecture is modular: the `ggml` tensor library provides a pluggable backend abstraction, while model-specific graph builders in `src/models/` construct computation graphs. The project is actively maintained with frequent releases and broad community adoption.
whisper.cpp is a high-performance C/C++ inference engine for OpenAI's Whisper automatic speech recognition (ASR) model, with additional support for a wide range of large language models (LLMs) via an integrated llama.cpp fork. The core library is dependency-free and uses the ggml tensor library to run on CPU, GPU (CUDA, Metal, Vulkan, OpenCL, SYCL, etc.), and specialized hardware (Apple Neural Engine, Hexagon DSP, etc.). It provides a clean C API, extensive language bindings (Go, Java, Ruby, JavaScript, Swift, etc.), and a rich set of example applications including a CLI transcriber, real-time microphone streaming, an HTTP server with OpenAI-compatible API, voice-controlled chess, and a talk-llama chatbot. The repository also includes model conversion scripts (PyTorch to ggml, CoreML, OpenVINO), quantization tools, and evaluation benchmarks (LibriSpeech, Earnings21). While primarily an application library, it contains research-oriented features like grammar-constrained decoding, beam search, and experimental model architectures. The codebase is actively maintained with a large community, though the sheer number of hardware backends and model variants introduces some maintenance complexity.