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NCA-AIIO - AI Infrastructure - Section 2.4

Articulate the advantages, challenges, and considerations of on-premise versus cloud infrastructure.

Describe the trade-offs between on-premise GPU infrastructure - with predictable costs, data sovereignty, and low-latency access - and cloud-based GPU instances that offer elasticity and lower upfront capital expenditure. Weigh these factors against workload characteristics such as continuous long-running training jobs versus sporadic or burst inference demand.

More in this domain

Identify hardware requirements for specific AI training task use cases.Section 2.1
Scale a GPU infrastructure for different use cases.Section 2.2
Identify key concepts and high-level specifications for power and cooling.Section 2.3
Identify key components and considerations of accelerated infrastructure clusters.Section 2.5
Identify facility requirements for accelerated infrastructure.Section 2.6
Determine networking requirements for AI workloads.Section 2.7
Identify and describe data centre networking protocols and key concepts.Section 2.8
Identify high-speed data centre network options and their use cases.Section 2.9
Explain the purpose and benefits of a DPU in a data centre.Section 2.10

Back to all AI Infrastructure objectives, or the NCA-AIIO cert hub.

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