Accelerating LLM inference with post-training weight and activation using AWQ and GPTQ on Amazon SageMaker AI

This article explains how to accelerate large language model (LLM) inference using post-training quantization (PTQ) techniques AWQ and GPTQ on Amazon SageMaker AI.

• PTQ reduces model size 2-8x by converting weights/activations to lower-bit integers without retraining
• W4A16 asymmetric quantization achieves ultra-low precision with minimal accuracy loss
• W8A8 enables full integer inference for maximum hardware utilization and speed
• W8A16 weight-only quantization provides safe baseline with 2-4x memory reduction
• AWQ uses activation-aware scaling to preserve critical weight channels at 4-bit precision
• GPTQ applies layer-by-layer error compensation using Hessian approximations for optimal compression
• Quantized models show 30-70% GPU memory reduction across Llama and Qwen models tested
• End-to-end latency improves 2-3x; throughput increases significantly at high concurrency
• SageMaker training jobs with llm-compressor library simplify quantization workflow
• Quantized models deploy on smaller GPU instances, reducing infrastructure costs substantially

PTQ enables cost-effective, scalable LLM deployment by dramatically reducing memory requirements and inference latency while maintaining model quality, making large models practical for production environments.