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Post-training quantization (PTQ) techniques like AWQ and GPTQ compress LLM weights from 16/32-bit to 4/8-bit integers, reducing model size by 2-8x without retraining. This enables deploying massive models (like DeepSeek-V3 or Llama 3.1 70B) on smaller GPU instances while maintaining near-original accuracy. The article explains weight/activation quantization strategies (W4A16, W8A8, etc.), compares AWQ's activation-aware scaling against GPTQ's error-compensation approach, and demonstrates implementation on Amazon SageMaker using vLLM and llm-compressor. Benchmark results across three models show 30-70% memory reduction, 2-3x latency improvements, and higher throughput at scale, making state-of-the-art LLMs production-viable.

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

Weights and activation techniques (WₓAᵧ)

Inference acceleration through PTQ techniques

Using Amazon SageMaker AI for inference optimization and model quantization