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

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.