2. 容量获取比较:根据我的项目和虚拟机的机器系列,比较 AI Hypercomputer 中当前和可用的使用选项及其具体特征,包括支持的 GPU 层级(集群化 GPU 和通用 GPU)、所需的配额类型、交付周期和结算模式。
3. Recommended acquisition path: Recommend the best capacity acquisition path for long-running clustered GPU workloads, medium-term reserved jobs, short-lived dense jobs, and general GPU deployments.
Format the comparison as a Markdown table (with columns for Consumption Option, Supported GPUs, Required Quota, and Billing Model), provide a ready-to-use gcloud CLI command to reserve capacity or create instances, and end with 2-3 questions about my target GPU series, node count, and duration to refine the action plan.expand_more
Act as an AI infrastructure architect. Help me obtain compute capacity in AI Hypercomputer based on my project's quotas, active reservations, and available regions.
Provide the following:
1. Project quota and reservation audit: Inspect my project's standard and preemptible GPU quotas and active reservations across regions, and identify which GPU machine series have available quota or active reservations compared to those that require a quota increase or reservation.
2. Capacity acquisition comparison: Compare current and available consumption options in AI Hypercomputer and their specific characteristics—including supported GPU tiers (clustered GPUs and general GPUs), required quota types, lead times, and billing models—based on my project and the machine series of my VMs.
3. Recommended acquisition path: Recommend the best capacity acquisition path for long-running clustered GPU workloads, medium-term reserved jobs, short-lived dense jobs, and general GPU deployments.
Format the comparison as a Markdown table (with columns for Consumption Option, Supported GPUs, Required Quota, and Billing Model), provide a ready-to-use gcloud CLI command to reserve capacity or create instances, and end with 2-3 questions about my target GPU series, node count, and duration to refine the action plan.