Deep dive
The full breakdown - performance, comparisons, and setup
GPTQ-for-LLaMa
GPTQ-for-LLaMa is a local-AI tool - 4 bits quantization of LLaMA using GPTQ.
Overview
I am currently focusing on AutoGPTQ and recommend using AutoGPTQ instead of GPTQ for Llama.
It can be used universally, but it is not the fastest and only supports linux.
Triton only supports Linux, so if you are a Windows user, please use WSL2.
AutoGPTQ-triton, a packaged version of GPTQ with triton, has been integrated into AutoGPTQ.
Quantization requires a large amount of CPU memory. However, the memory required can be reduced by using swap memory.
Depending on the GPUs/drivers, there may be a difference in performance, which decreases as the model size increases.(https://github.com/IST-DASLab/gptq/issues/1)
According to GPTQ paper, As the size of the model increases, the difference in performance between FP16 and GPTQ decreases.
Basically, 4-bit quantization and 128 groupsize are recommended.
You can also export quantization parameters with toml+numpy format.
GPTQ-for-LLaMa is open-source, written primarily in Python, with 3,069 GitHub stars under the Apache 2.0 license. It was last updated on 2024-07-13.
Key capabilities
From the project's documentation:
- torch: tested on v2.0.0+cu117
- transformers: tested on v4.28.0.dev0
- datasets: tested on v2.10.1
- safetensors: tested on v0.3.0
Install
A quick way to get started (always check the official docs for the latest):
pip3 install torch torchvision torchaudioHow it fits a local-AI stack
GPTQ-for-LLaMa runs on your own hardware, so pair it with a model and a GPU sized to your needs. Use the VRAM calculator to pick a model that fits your card, and see what you can run for hardware guidance. Related local-AI tools in the directory:
Sources
- Source code & docs: qwopqwop200/GPTQ-for-LLaMa
Stats from GitHub, 2026-10-26.
Frequently asked
Quick answers to common questions
What is GPTQ-for-LLaMa?
GPTQ-for-LLaMa is a other tool for local AI workloads. 4 bits quantization of LLaMA using GPTQ
Is GPTQ-for-LLaMa free and open source?
Yes, GPTQ-for-LLaMa has 3,069 GitHub stars and is licensed under Apache 2.0. You can self-host it for free on linux, windows.
What platforms does GPTQ-for-LLaMa support?
GPTQ-for-LLaMa runs on linux, windows.
What hardware do I need for GPTQ-for-LLaMa?
The hardware requirements depend on which models you run. Check our hardware directory for compatible GPUs and systems. GPTQ-for-LLaMa has 3,069 GitHub stars and an active community.
Does GPTQ-for-LLaMa support GPU acceleration?
GPTQ-for-LLaMa supports GPU acceleration via CUDA, Metal, or Vulkan depending on your platform. For the best performance, pair it with an NVIDIA RTX 4090 or 5090.
What are the best alternatives to GPTQ-for-LLaMa?
Popular alternatives include other other tools in our directory. Browse our full collection at /tool for comparisons, community reviews, and benchmark data to find the right fit for your workflow.
How much does GPTQ-for-LLaMa cost?
GPTQ-for-LLaMa is free-open-source. It is completely free and open source to self-host.
Pairs well with
Complementary tools, models, and hardware