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gptq

Compress large language models with GPTQ 4-bit post-training quantization.

Updated May 4, 2026
Search Tags:#deployment#llm#calibration#quantization#4-bit#gptq#autogptq
One-click install
npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill gptq-supporter09
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gptq
Source: https://github.com/Supporter09/Face_Anti_Spoofing_Biometric/tree/main/.claude/skills/gptq
Command: npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill gptq-supporter09

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

GPTQ reduces the memory footprint of large language models while keeping accuracy high, making it practical to deploy models that would otherwise exceed consumer GPU limits.

Core Features & Use Cases

  • Post-training 4-bit quantization: Compress pretrained LLMs into efficient 4-bit checkpoints with minimal perplexity loss.
  • Calibration and quality tuning: Choose representative calibration data, adjust group size, dampening, and activation ordering, and validate quality after quantization.
  • Deployment integration: Load GPTQ models through Transformers, AutoGPTQ, PEFT, vLLM, TGI, or LangChain for real-world inference and fine-tuning workflows.
  • Use Case: A team wants to serve a 70B model on a single workstation GPU, so they quantize it with GPTQ, test perplexity, and deploy it with the fastest compatible backend.

Quick Start

Use the gptq skill to recommend the best 4-bit quantization configuration for your model, calibration data, and target GPU, then generate the loading and deployment steps you should follow.

Frequently Asked Questions about gptq

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

FAQPage Schema
How do I run a 70B LLM on a smaller GPU using 4-bit quantization?▼

You can run a 70B LLM on a smaller GPU by applying GPTQ post-training 4-bit quantization to compress the model checkpoint, reducing memory footprint while preserving inference quality for deployment.

What calibration data do I need for GPTQ post-training quantization?▼

GPTQ post-training quantization requires representative calibration data to tune group size, dampening, and activation ordering, ensuring the 4-bit model maintains high accuracy and minimal perplexity loss.

Does GPTQ 4-bit quantization work with vLLM and TGI backends?▼

GPTQ 4-bit quantization works with vLLM and TGI backends, alongside Transformers, AutoGPTQ, PEFT, and LangChain, enabling real-world inference and fine-tuning workflows for deployment.

Can I fine-tune a 4-bit quantized LLM using PEFT?▼

You can fine-tune a 4-bit quantized LLM using PEFT, integrating the GPTQ model through compatible runtime backends like Transformers and AutoGPTQ for efficient real-world workflows.

What is the best way to deploy a 405B model on consumer GPUs?▼

The best way to deploy large 70B to 405B models on consumer GPUs is GPTQ post-training 4-bit quantization, which compresses checkpoints to fit memory limits while validating quality through perplexity testing.

Why does my LLM lose accuracy after 4-bit quantization?▼

Accuracy loss after 4-bit quantization often stems from poorly chosen calibration data or incorrect group size and dampening settings, requiring careful configuration tuning and quality validation to minimize perplexity.

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