QuantLLM: A One-Stop Toolkit for Large Language Model Quantization and Deployment

QuantLLM is an open-source Python library designed to simplify the quantization, fine-tuning, and multi-format export processes of large language models (LLMs). It supports 4-bit/8-bit quantization, multiple export formats such as GGUF/ONNX/MLX, and provides a unified turbo() API that allows developers to complete the entire workflow from loading to deployment with a single line of code.

As the parameter size of large language models (LLMs) grows from billions to hundreds of billions, how to efficiently run these models on consumer-grade hardware has become a core challenge for developers. The traditional model loading and inference workflow often involves multiple steps: environment configuration, quantization conversion, format adaptation, deployment optimization—each step can be a bottleneck. QuantLLM was created to address this pain point. It provides a unified abstraction layer that encapsulates complex quantization, fine-tuning, and export processes into concise APIs, allowing developers to focus on the application itself rather than the underlying infrastructure.

QuantLLM is an open-source Python library designed for developers, researchers, and teams who want to efficiently fine-tune and deploy large language models. Its core philosophy is 'one line of code, full workflow coverage'—from model loading, automatic quantization, fine-tuning to multi-format export, all can be done through a unified interface. Compared to traditional quantization solutions, QuantLLM's uniqueness lies in its highly integrated design. Developers do not need to manually handle tedious steps such as BitsAndBytesConfig, LoRA configuration, GGUF conversion, etc. They only need to call the turbo() function and specify the target format, and the library will automatically complete the remaining optimization work.

QuantLLM provides a range of features optimized for production environments:

The library automatically detects available GPU memory and computing capabilities, dynamically selecting the optimal quantization strategy. When compatible hardware is detected, it automatically enables Flash Attention 2 to accelerate inference, while configuring memory management strategies to avoid memory overflow.

QuantLLM supports multiple quantization levels from 2-bit to 8-bit, each tailored for different use cases:

• Q4_K_M (Recommended)：4-bit quantization, achieving the best balance between model quality and size
• Q5_K_M：5-bit quantization, suitable for scenarios with high quality requirements
• Q8_0：8-bit quantization, close to the original model quality, suitable for precision-sensitive applications
• Q2_K：2-bit quantization, extreme compression, suitable for environments with extremely limited resources

This is a key highlight of QuantLLM. The same model can be easily exported to multiple formats to adapt to different deployment environments:

Format	Use Case	Export Command
GGUF	llama.cpp, Ollama, LM Studio	model.export("gguf")
ONNX	ONNX Runtime, TensorRT	model.export("onnx")
MLX	Apple Silicon (M1/M2/M3/M4)	model.export("mlx")
SafeTensors	HuggingFace Ecosystem	model.export("safetensors")

This flexibility means developers can use HuggingFace format for rapid iteration during development, and convert to GGUF or ONNX during deployment for better inference performance, without maintaining multiple sets of code.

QuantLLM's API design follows the principle of minimalism. Here are a few typical usages: