GGML

GGML (Gears General Machine Learning) is a C/C++ library designed for machine learning inference, specifically optimized for local execution on consumer hardware. It serves as the foundational backend for the GGUF format, which standardizes the storage of quantized model weights, metadata, and tensors.

Core Concepts

GGUF Format: The successor to GGML’s binary format, allowing for flexible metadata and tensor storage. It is the standard output for models converted from PyTorch/Transformers for use in local inference engines.
Quantization: GGML/GGUF supports various quantization levels (e.g., Q4_K_M, Q8_0) to reduce memory footprint and increase inference speed with minimal accuracy loss.
Inference Engines: GGML is the engine behind popular local LLM runners:
- llama.cpp: The primary reference implementation.
- Ollama: Uses GGUF under the hood for easy model management.
- lm-studio: Provides a GUI for GGUF-based inference.

A practical guide for deploying the Qwen 3.8-27B model in quantized GGUF format across major local inference tools.

Source: Qwen 3.8-27B GGUF Local Deployment via llama.cpp, Ollama, LM Studio
Key Details:
- Presenter: Fahd Mirza
- Focus: Local installation and testing of the 27B parameter model.
- Tools Covered: llama.cpp, Ollama, and lm-studio.
- Date: 2026-08-15
Reference: Qwen 3.8-27B GGUF Local Deployment via llama.cpp, Ollama, LM Studio