问HN：租用GPU时，您更关心价格、可靠性还是设置？
在为机器学习工作负载租用GPU时，您如何选择提供商？现在有很多GPU云服务和GPU共享平台，许多平台似乎提供类似的GPU选项……<p>那么，如果GPU型号相同且功能相似，您是否主要选择价格最低的提供商？还是可靠性、可用性、网络/存储以及设置环境对您来说更重要？<p>我想了解真正的痛点是什么，以便在选择提供商时做出正确的决策。<p>我也很好奇：您更愿意自己手动比较提供商，还是使用一个根据您的工作负载推荐合适的GPU/提供商的服务？
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When renting GPUs for ML workloads, how do you actually choose between providers? There are now so many GPU cloud / GPU sharing platforms, and many of them seem to offer similar GPU options....<p>So, if the GPU model is the same and providing similar functionalities, do you mostly choose the cheapest provider? Or do reliability, availability, networking/storage, and setup environment matter more for you?<p>Trying to understand what the real pain point is and make right decision for me when I am choosing the provider.<p>Also curious: would you rather manually compare providers yourself, or use a service that recommends the right GPU/provider based on your workload?