混沌工程：在 AI 時代重建系統韌性
儀表板綠燈不代表系統安全。當 LLM 延遲飆升、向量資料庫狀態不一致時，傳統監控往往失效。本文探討如何從「預防」轉向「韌性驗證」，並提供在 AI 導入期建立故障注入框架的實戰指南。
AI Infrastructure
Chaos Engineering: Rebuilding AI System Resilience
A green dashboard doesn’t mean the system is safe. When LLM latency spikes or vector database states become inconsistent, traditional monitoring often fails. This article explores how to shift from “prevention” to “resilience validation,” and provides a practical guide to building a fault injection framework during AI adoption.
DGX Spark：什麼樣的工作流值得自有 GPU？80% 的工程債在硬體後面
邊緣 AI 推理該買 DGX Spark 還是繼續付雲端費用？算過一次帳的人都知道，硬體不是 sticker price 的問題——是後面 80% 看不見的工程債：模型量化、推理框架選型、散熱、運維、跨機 RDMA。雲端是租金，自有 GPU 是房貸加裝修。三個自評問題判斷你的工作流值不值得 on-prem：日均 token 量、延遲敏感度、模型迭代頻率，以及 break-even 怎麼算才不會被 GPU spec 表騙。
DGX Spark: What Workflows Justify Owning a GPU? 80% of the Engineering Debt Comes After the Hardware
Should your edge AI inference run on a DGX Spark or stay on cloud APIs? Anyone who’s run the numbers knows the sticker price is the easy part—the hidden 80% is engineering debt: quantization, inference framework choice, thermals, ops, cross-node RDMA. Cloud is rent; owning GPUs is a mortgage plus renovation. Three self-assessment questions to decide if your workload deserves on-prem—daily token volume, latency sensitivity, model iteration cadence—and how to compute break-even without getting fooled by the GPU spec sheet.