Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 10 discussion

A: Deploy optimized small language models (SLMs) on edge devices. Explanation: Deploying optimized small language models (SLMs) on edge devices ensures low latency because the inference happens directly on the device without relying on cloud communication. Small language models are lightweight and designed to run efficiently on devices with limited resources, making them ideal for edge computing.

Lowest latency = Model has to be deployed on the Edge device Only SLM can be deployed on Edge device due to capacity constraints

Quando o objetivo é inferência com a menor latência possível, a melhor abordagem é executar o modelo diretamente no dispositivo de borda (edge). SLMs (Small Language Models) são projetados para serem leves, rápidos e eficientes, o que os torna ideais para: Dispositivos com recursos limitados Tempo de resposta imediato . Execução offline ou com pouca conectividade

Optimized small language models (SLMs) are specifically designed to run efficiently on edge devices with limited resources (such as memory and processing power). Deploying smaller, optimized models directly on the edge devices allows for near-instantaneous inference with minimal latency, as the data doesn't need to travel to a central server for processing.

Lowest latency possible - SLM

Low latency with edge devices -> SLM

A is good - Minimal latency: SLMs are designed to run efficiently on resource-constrained devices, offering fast inference directly on the device.

SLM on edge devices

SLM on edge devices is the correct solution.

Using Optimized Small Language Models (SLMs) on edge devices is the best choice because they are designed to run efficiently within the resource constraints of edge hardware. This minimizes latency and helps deliver fast inference times while using less computational power and memory. The problem with trying to use centralized APIs is the associated latentcy.