How much memory does AWQ save?

AWQ typically reduces model memory requirements by 2.5x to 4x. For example, a 70B model can be reduced from 140GB in FP16 to approximately 35GB in 4-bit AWQ.

Which GPUs are compatible with AWQ's fastest kernels?

AWQ's highly optimized Marlin kernels require NVIDIA Ampere architecture or newer (A100, H100, RTX 30xx, and RTX 40xx series).

Can I use AWQ for fine-tuning models?

AWQ is primarily designed for inference optimization. For quantization-aware fine-tuning, techniques like QLoRA using bitsandbytes are generally more appropriate.

What makes AWQ better than GPTQ for model quantization?

AWQ protects salient weights based on activation patterns rather than just weights, leading to better accuracy preservation, especially for instruction-tuned models, and faster inference via Marlin kernels.

AWQ LLM Quantization

Name: AWQ LLM Quantization
Author: zechenzhangAGI

byzechenzhangAGI

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384

Data Science & ML

Optimizes Large Language Models using activation-aware 4-bit quantization to achieve 3x inference speedups and significant memory reduction with minimal accuracy loss.

About

AWQ (Activation-aware Weight Quantization) is a state-of-the-art compression technique that preserves the most important weights in a model based on activation patterns, winning the MLSys 2024 Best Paper Award. This skill enables developers to deploy large-scale models like Llama 3 or Mistral on hardware with limited VRAM by reducing model size by up to 4x. It provides comprehensive patterns for using AutoAWQ, integrating with high-performance backends like vLLM, and utilizing optimized Marlin kernels for Ampere and Hopper GPUs, making it ideal for production-grade AI inference.

Key Features

Support for optimized Marlin, GEMM, and GEMV kernel backends
384 GitHub stars
Up to 3x inference speedup compared to standard FP16 precision
Custom calibration workflows for domain-specific and multimodal models
4-bit weight quantization with less than 5% accuracy degradation
Native integration patterns for vLLM and HuggingFace Transformers

Use Cases

Optimizing production inference throughput for instruction-tuned chat models
Deploying 70B parameter models on consumer GPUs or limited cloud instances
Reducing VRAM footprint for multimodal models like LLaVA and Qwen-VL

About

Key Features

Support for optimized Marlin, GEMM, and GEMV kernel backends
384 GitHub stars
Up to 3x inference speedup compared to standard FP16 precision
Custom calibration workflows for domain-specific and multimodal models
4-bit weight quantization with less than 5% accuracy degradation
Native integration patterns for vLLM and HuggingFace Transformers

Use Cases

Optimizing production inference throughput for instruction-tuned chat models
Deploying 70B parameter models on consumer GPUs or limited cloud instances
Reducing VRAM footprint for multimodal models like LLaVA and Qwen-VL