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gptq

Quantize large language models to 4-bit with the GPTQ algorithm.

2|Updated Feb 4, 2026
Search Tags:#memory optimization#quantization#llm optimization#4-bit#gptq#inference speed
One-click install
npx skills add https://github.com/zhuangbiaowei/smart_bot --skill gptq-zhuangbiaowei
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gptq
Source: https://github.com/zhuangbiaowei/smart_bot/tree/main/skills/gptq
Command: npx skills add https://github.com/zhuangbiaowei/smart_bot --skill gptq-zhuangbiaowei

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires auto-gptq, transformers, optimum, peft, datasets, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of running large language models (LLMs) on hardware with limited GPU memory by applying post-training 4-bit quantization, significantly reducing memory footprint and increasing inference speed.

Core Features & Use Cases

  • Memory Reduction: Fit massive models (70B+) onto consumer-grade GPUs.
  • Speed Improvement: Achieve 3-4x faster inference compared to FP16.
  • Accuracy Preservation: Maintain <2% perplexity degradation.
  • Use Case: Deploy a 70B parameter LLM on a single RTX 4090 for real-time text generation tasks.

Quick Start

Use the gptq skill to load the pre-quantized model 'TheBloke/Llama-2-7B-Chat-GPTQ' onto your CUDA device.

Frequently Asked Questions about gptq

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fit a 70B parameter LLM onto a single consumer GPU for inference?▼

Apply post-training 4-bit quantization to the LLM using the GPTQ algorithm to compress memory usage by 4x, enabling deployment of massive models on consumer GPUs like a single RTX 4090.

What is the accuracy trade-off when applying 4-bit quantization to large language models?▼

4-bit quantization using GPTQ achieves minimal accuracy loss, maintaining less than 2% perplexity degradation compared to the original FP16 model while delivering 3-4x faster inference speed.

Can I use transformers and PEFT with a GPTQ quantized model?▼

Yes, GPTQ quantization integrates directly with the transformers and PEFT frameworks, allowing you to load pre-quantized models like 'TheBloke/Llama-2-7B-Chat-GPTQ' and perform parameter-efficient fine-tuning.

How do I load a pre-quantized GPTQ model onto my CUDA device?▼

You can load a pre-quantized model such as 'TheBloke/Llama-2-7B-Chat-GPTQ' directly onto your CUDA device using the integrated transformers library and auto-gptq dependencies for immediate inference.

What inference kernels does GPTQ support for optimizing LLM inference speed?▼

GPTQ supports various optimized kernel backends, including ExLlamaV2 and Marlin, to deliver 3-4x faster inference speeds compared to standard FP16 execution on consumer GPUs.

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