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gptq

Perform post-training 4-bit quantization for large language models.

Updated Jan 12, 2026
Search Tags:#quantization#llm optimization#4-bit#gptq#inference speed#consumer gpu
One-click install
npx skills add https://github.com/MesferAli/XCircle --skill gptq-mesferali
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gptq
Source: https://github.com/MesferAli/XCircle/tree/main/.claude/skills/gptq
Command: npx skills add https://github.com/MesferAli/XCircle --skill gptq-mesferali

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of deploying large language models (LLMs) on resource-constrained hardware, such as consumer GPUs, by significantly reducing their memory footprint and increasing inference speed.

Core Features & Use Cases

  • 4-bit Quantization: Compresses LLMs to 4-bit precision with minimal accuracy loss, enabling larger models to fit on smaller GPUs.
  • Memory Reduction: Achieves up to 4x memory savings compared to FP16 models.
  • Inference Speedup: Provides 3-4x faster inference compared to FP16.
  • Use Case: Deploying a 70B parameter LLM on a single consumer GPU (e.g., RTX 4090) for tasks like text generation, summarization, or chatbot applications.

Quick Start

Use the gptq skill to load the Llama-2-7B-Chat-GPTQ model and generate text for a given prompt.

Frequently Asked Questions about gptq

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

FAQPage Schema
How do I compress a large language model to 4-bit for inference on a consumer GPU?▼

You can compress a large language model to 4-bit for consumer GPU inference by applying post-training group-wise quantization. This technique reduces memory usage by up to 4x and accelerates inference speeds by 3-4x compared to standard FP16 precision.

Can I use 4-bit quantization with PEFT and transformers for QLoRA fine-tuning?▼

Yes, 4-bit quantization integrates seamlessly with transformers and PEFT to enable QLoRA fine-tuning workflows. This combination allows you to fine-tune large language models efficiently on resource-constrained hardware like consumer GPUs.

What is the memory reduction when deploying an LLM with GPTQ quantization?▼

GPTQ quantization achieves up to 4x memory savings compared to FP16 models. This significant memory reduction allows you to deploy large 70B parameter language models on a single consumer GPU like an RTX 4090.

Does 4-bit post-training quantization cause significant accuracy loss for LLMs?▼

4-bit post-training quantization compresses large language models with minimal accuracy loss. By applying group-wise quantization techniques, it maintains model performance while delivering 3-4x faster inference speeds and reduced memory footprints.

What dependencies do I need to run 4-bit LLM quantization and inference?▼

To perform 4-bit LLM quantization and inference, you need the auto-gptq, transformers, optimum, and peft dependencies. These libraries provide the necessary framework support for model compression, optimization, and QLoRA fine-tuning workflows.

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