From the course: AI Coding: Impacts on System Design and Architecture

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Optimize resource management for sustainable AI workloads

Optimize resource management for sustainable AI workloads

From the course: AI Coding: Impacts on System Design and Architecture

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Optimize resource management for sustainable AI workloads

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- [Instructor] How do you build AI systems that are powerful enough to scale, but efficient enough to sustain? As AI systems grow in size and complexity, so does their resource footprint. From training massive models to handling real-time inference, the compute, storage, and energy demands can quickly add up. Not just in costs, but in environmental impact. This makes sustainable architecture a strategic imperative. Start by right-sizing, not over-sizing your architecture. This means matching the compute to the workload. Use GPU or TPU resources where needed, but don't default to them if CPU-based inference is sufficient. Use auto-scaling policy so you're not running idle instances around the clock. And always monitor resource utilization over time. Your architecture should flex to meet demand, not just operate at max capacity by default. Choose efficient model architectures. Bigger isn't always better. Wherever possible, opt for smaller, more efficient models, or quantized versions of…

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