Introduction to ggml

Source: HuggingFace Date: 2024-08-13 URL: https://huggingface.co/blog/introduction-to-ggml

Summary

Educational tutorial: Introduction to ggml — the C/C++ tensor computation library underlying llama.cpp, Ollama, LM Studio, GPT4All, and others. Covers core concepts (ggml_context, ggml_cgraph, ggml_backend, ggml_backend_sched), compilation (GCC/Clang only, <1MB binary), and two working examples (matrix multiplication without backends, production-style with GPU backend abstraction). Supports x86_64, ARM, Apple Silicon, CUDA, Metal, Vulkan. No benchmark numbers.

Implications

Open-weights ecosystem health. ggml being the foundation of llama.cpp, Ollama, LM Studio, and GPT4All means it underlies the majority of consumer-hardware open-weights model inference. A clear tutorial for the library itself — rather than just its applications — enables developers to build new inference tools directly on ggml rather than working through higher-level abstractions, which matters for novel hardware targets and specialized deployment scenarios.

Transformers library trajectory. ggml’s <1MB binary vs PyTorch’s hundreds of MB is the key deployment advantage for constrained environments. As open-weights models move to phones, embedded hardware, and WebAssembly targets, ggml’s minimal dependency footprint makes it the inference foundation for contexts where PyTorch cannot realistically run. HF publishing this tutorial signals awareness that the ggml ecosystem is a peer to the Transformers stack, not a niche alternative.