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awq-quantization

Quantize large language models to 4-bit with activation-aware AWQ.

1.0k|117|Updated Feb 26, 2026
Search Tags:#calibration#quantization#model-compression#4bit#awq#activation-aware#inference-speed
One-click install
npx skills add https://github.com/OpenLAIR/dr-claw --skill awq-quantization-openlair
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: awq-quantization
Source: https://github.com/OpenLAIR/dr-claw/tree/main/skills/optimization/awq
Command: npx skills add https://github.com/OpenLAIR/dr-claw --skill awq-quantization-openlair

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

AWQ enables activation-aware 4-bit quantization to reduce memory and improve inference speed for large language models while preserving accuracy.

Core Features & Use Cases

  • Activation-aware 4-bit quantization for efficient LLM deployment.
  • Supports Marlin, GEMM, GEMV kernels and multi-GPU setups for fast inference.
  • Suitable for instruction-tuned and multimodal models requiring compact deployments.

Quick Start

Quantize your model to 4-bit AWQ using the recommended kernels and validate inference performance.

Frequently Asked Questions about awq-quantization

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

FAQPage Schema
How does activation-aware 4-bit quantization work for large language models?▼

Activation-aware 4-bit quantization scales weights based on activation magnitudes to preserve salient weights, minimizing accuracy loss while significantly reducing memory footprints and accelerating LLM inference.

Can I quantize multimodal and instruction-tuned models using AutoAWQ?▼

Yes, AutoAWQ quantization supports instruction-tuned and multimodal models for compact deployment, allowing you to compress these architectures to 4-bit while maintaining inference accuracy across supported hardware.

What inference kernels and multi-GPU setups are supported for 4-bit model quantization?▼

4-bit quantization supports Marlin, GEMM, and GEMV kernels optimized for GPU acceleration, alongside multi-GPU setups to maximize inference speed and manage large model deployments efficiently.

What dependencies do I need to configure AWQ quantization backends and calibration data?▼

You need AutoAWQ, Transformers (>=4.45.0), and Torch (>=2.0.0) to configure AWQ quantization backends, define calibration datasets, and adjust group-size settings for optimal compression.

Is 4-bit AWQ quantization the best way to reduce memory footprint without losing accuracy?▼

4-bit AWQ quantization is highly effective for memory reduction because it uses activation-aware scaling to protect salient weights, offering a superior balance of inference speed and minimal accuracy loss compared to standard methods.

When should I not use 4-bit model compression for inference acceleration?▼

You should avoid 4-bit model compression if your target hardware lacks supported Marlin, GEMM, or GEMV kernels, or if your deployment cannot accommodate the required AutoAWQ, Transformers, and Torch dependencies.

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