Open Source C LLM Inference Tools

whisper.cpp is a lightweight, C/C++ reimplementation of OpenAI’s Whisper automatic speech recognition (ASR) model—designed for efficient, standalone transcription without external dependencies. The entire high-level implementation of the model is contained in whisper.h and whisper.cpp. The rest of the code is part of the ggml machine learning library. The command downloads the base.en model converted to custom ggml format and runs the inference on all .wav samples in the folder samples. whisper.cpp supports integer quantization of the Whisper ggml models. Quantized models require less memory and disk space and depending on the hardware can be processed more efficiently.

The llama.cpp project enables the inference of Meta's LLaMA model (and other models) in pure C/C++ without requiring a Python runtime. It is designed for efficient and fast model execution, offering easy integration for applications needing LLM-based capabilities. The repository focuses on providing a highly optimized and portable implementation for running large language models directly within C/C++ environments.

Colibrì is an open-source inference engine designed to run frontier-scale mixture-of-experts (MoE) models on consumer and heterogeneous hardware. It treats VRAM, system RAM, and NVMe storage as a unified memory hierarchy, dynamically placing and streaming model weights where they can be accessed most efficiently. This architecture enables users to run models ranging from 7 billion to 2.8 trillion parameters without requiring the entire model to fit in expensive GPU memory. Written primarily in pure C, Colibrì has no core engine dependencies and can operate without a GPU, although CUDA, Metal, Vulkan, and other acceleration options can improve performance. A consistent command-line interface, OpenAI-compatible API, web dashboard, and desktop application support multiple model families, including GLM, DeepSeek, Kimi, Qwen, Inkling, and OLMoE. Colibrì also serves as an open research platform for testing model placement, caching, compression, speculative decoding, storage I/O, & more.

A real time inference engine for temporal logical specifications, which is able to acquire, process and generate any binary or real signal through POSIX IPC, files or UNIX sockets. Specifications of signals and dynamic systems are represented as special graphs and executed in real time, with a predictable sampling time of few milliseconds. Real time signal processing, dynamic system control, state machine modeling and logical property verification are some fields of application of this software. The accepted language provides timed logic and mathematical operators, conditional operators, interval operators, bounded quantifiers and parametrization of signals.