GGML
also: Georgi Gerganov's Machine Learning
GGML is a C library for machine learning that enables efficient inference of large language models on consumer hardware with minimal memory and computational requirements.
GGML stands for Georgi Gerganov's Machine Learning library. It's designed to run quantized large language models (LLMs) locally on standard CPUs and GPUs without requiring high-end servers or cloud APIs.
The library achieves efficiency through quantization—reducing model precision (e.g., from 32-bit floats to 4-bit or 8-bit integers)—which dramatically cuts memory usage and speeds up computation while maintaining reasonable accuracy. This makes models like Llama, Mistral, and others practical to run on laptops and edge devices.
GGML is widely used in popular projects like llama.cpp and ollama, which provide user-friendly interfaces for running LLMs locally. A typical workflow: download a quantized GGML model file (`.gguf` format), load it with a compatible tool, and run inference without internet access or external services.