AI Resource Efficiency knowledge base
AIRE is designed to grow into a large educational library. These are the first recurring topics.
Local vs cloud AI
Compare quality, privacy, scalability, recurring cost and operational control.
How much GPU do you really need?
Model size, quantization, context and concurrency matter more than marketing labels.
AI subscription overlap
Multiple providers can improve resilience, but redundant paid tools can quietly increase cost.
Hybrid routing
Route easy, repetitive workloads to cheap resources and difficult tasks to stronger models.
AI electricity cost
Local inference has a real energy cost. Compare utilization and electricity with API/cloud alternatives.
Storage and context
Large context windows do not replace a clean document, retrieval and memory architecture.
AI sovereignty
Provider independence improves resilience when models, prices, quotas or policies change.
Private AI workloads
Confidential data may justify local or controlled infrastructure even when cloud is technically stronger.
When automation is worth it
Automate repetitive work when the workflow is stable enough to measure quality and rework.
Good-enough optimization
Define stop criteria for quality, cost, latency and human effort instead of optimizing forever.