Topic: LLM Inference

We highlight how post-training quantization (PTQ) techniques like AWQ and GPTQ are revolutionizing large language model (LLM) deployments by significantly reducing hardware requirements without sacrificing substantial performance. These quantized models integrate seamlessly into Amazon SageMaker AI with minimal code, addressing the common challenges of high inference costs and the environmental footprint of modern AI. By focusing on weight and activation optimization, developers can now run powerful LLMs on resource-constrained instances while maintaining low latency and high throughput. Our comprehensive guide delves into the core principles of PTQ, offering a practical demonstration for quantizing any model of your choice and deploying it on Amazon SageMaker efficiently. This robust approach empowers organizations to balance operational efficiency and model accuracy, effectively lowering the barrier to entry for high-performance, production-grade AI applications on the AWS cloud ecosystem.