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Optimizing GPU Infrastructure for AI Workloads

Choosing the right GPU strategy saves thousands in compute costs.

Matching Workloads to Infrastructure

• On-prem is for constant, high-utilization tasks.
• Cloud is for bursty, variable workloads.
• Watch your egress costs when moving large models.

workload = {'hours': 720, 'rate': 2.50, 'data': 10}
total = calculate_cost(workload['hours'], workload['rate'], workload['data'])
print(f"Estimated monthly cost: ${total}")
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Key takeaway: Strategic infrastructure planning ensures you never pay for idle silicon.

https://youtu.be/_l67b6Ai2aE

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