AI Data Platform AI Data Platform

"Our GPUs sit at 35% utilization between jobs, but we still can't serve more users.任務空檔 GPU 只有 35% 利用率，但還是無法服務更多使用者。"

KV Cache exhaustion causes OOM and hard concurrent request caps. Multiple inference instances can't share a KV Cache pool, leading to redundant compute waste. Storage I/O becomes a bottleneck during checkpoint loading, and it's hard to dynamically allocate GPU resources across models. KV Cache 用完就 OOM，concurrent request 上限卡死。多個推論實例無法共享 KV Cache pool，造成重複計算浪費。存儲 I/O 在 checkpoint loading 時成為瓶頸，也難以動態分配 GPU 資源給不同模型。

"Managing separate GPU racks, storage arrays, and switches is eating my team alive.GPU 機櫃、儲存陣列、交換器各自管理，快把團隊耗盡了。"

Three independent systems — compute, storage, and network — each need their own ops team. AI workload power density breaks traditional data center planning, while 6–9 month procurement cycles can't keep up with business expansion. Right-sizing is equally painful: over-provision wastes budget, under-provision breaks SLA. 運算、存儲、網路三套獨立系統各自維護，人力成本高。AI workload 的 power density 讓傳統機房規劃失效，6–9 個月的採購周期也跟不上業務擴張。Right-size 同樣困難：over-provision 浪費預算，under-provision 則影響 SLA。

"We want private LLMs, but the complexity and upfront cost are paralyzing us.我們想要私有 LLM，但複雜度與前期成本讓我們動彈不得。"

Public cloud AI API costs exceed budget at production scale, and sensitive data can't leave the country — so OpenAI and Azure aren't viable. With no in-house GPU infra engineers, outsourcing risks vendor lock-in, and it's unclear which open-source LLM fits the business. 公有雲 AI API 費用在規模化後超出預算，敏感資料不能離境，無法直接用 OpenAI/Azure。內部缺乏 GPU infra 工程師，外包又怕 vendor lock-in，也不確定哪個開源 LLM 適合自身業務。

"Our compliance team vetoed cloud AI. We're stuck running nothing.合規團隊否決了雲端 AI，我們只能原地踏步。"

Regulations require inference on-premise under GDPR and healthcare privacy laws. Every AI inference needs a complete audit log that public cloud can't provide, and training data with sensitive customer info can't go to external services. Passing ISO 27001 / SOC 2 audits makes external AI services especially hard to approve. 法規要求推論運算必須在本地完成（GDPR、醫療個資法）。每一次 AI 推論需要完整 audit log，公有雲無法提供；模型訓練資料含客戶敏感資訊，也無法上傳外部服務。要通過 ISO 27001 / SOC 2 審查，外部 AI 服務更難獲准。

"Our researchers wait days in GPU queues. Science doesn't wait.研究員在 GPU 排隊要等好幾天，科學不等人。"

Shared GPU cluster queues stretch experiment cycles three to five times. TB-scale training datasets hit storage throughput limits, and teams on PyTorch, JAX, and TensorFlow compete for the same resources. Grant-driven procurement and infrastructure lifecycle rhythms are completely out of sync. 共用 GPU cluster 排隊等候，實驗周期拖長 3–5 倍。TB 級訓練資料集受存取速度限制，PyTorch、JAX、TensorFlow 多團隊也互相搶資源。計畫型採購與 infrastructure lifecycle 的節奏完全脫節。

"Our GPUs sit at 35% utilization between jobs, but we still can't serve more users.任務空檔 GPU 只有 35% 利用率，但還是無法服務更多使用者。"

KV Cache exhaustion causes OOM and hard concurrent request caps. Multiple inference instances can't share a KV Cache pool, leading to redundant compute waste. Storage I/O becomes a bottleneck during checkpoint loading, and it's hard to dynamically allocate GPU resources across models. KV Cache 用完就 OOM，concurrent request 上限卡死。多個推論實例無法共享 KV Cache pool，造成重複計算浪費。存儲 I/O 在 checkpoint loading 時成為瓶頸，也難以動態分配 GPU 資源給不同模型。

"Managing separate GPU racks, storage arrays, and switches is eating my team alive.GPU 機櫃、儲存陣列、交換器各自管理，快把團隊耗盡了。"

Three independent systems — compute, storage, and network — each need their own ops team. AI workload power density breaks traditional data center planning, while 6–9 month procurement cycles can't keep up with business expansion. Right-sizing is equally painful: over-provision wastes budget, under-provision breaks SLA. 運算、存儲、網路三套獨立系統各自維護，人力成本高。AI workload 的 power density 讓傳統機房規劃失效，6–9 個月的採購周期也跟不上業務擴張。Right-size 同樣困難：over-provision 浪費預算，under-provision 則影響 SLA。

"We want private LLMs, but the complexity and upfront cost are paralyzing us.我們想要私有 LLM，但複雜度與前期成本讓我們動彈不得。"

Public cloud AI API costs exceed budget at production scale, and sensitive data can't leave the country — so OpenAI and Azure aren't viable. With no in-house GPU infra engineers, outsourcing risks vendor lock-in, and it's unclear which open-source LLM fits the business. 公有雲 AI API 費用在規模化後超出預算，敏感資料不能離境，無法直接用 OpenAI/Azure。內部缺乏 GPU infra 工程師，外包又怕 vendor lock-in，也不確定哪個開源 LLM 適合自身業務。

"Our compliance team vetoed cloud AI. We're stuck running nothing.合規團隊否決了雲端 AI，我們只能原地踏步。"

Regulations require inference on-premise under GDPR and healthcare privacy laws. Every AI inference needs a complete audit log that public cloud can't provide, and training data with sensitive customer info can't go to external services. Passing ISO 27001 / SOC 2 audits makes external AI services especially hard to approve. 法規要求推論運算必須在本地完成（GDPR、醫療個資法）。每一次 AI 推論需要完整 audit log，公有雲無法提供；模型訓練資料含客戶敏感資訊，也無法上傳外部服務。要通過 ISO 27001 / SOC 2 審查，外部 AI 服務更難獲准。

"Our researchers wait days in GPU queues. Science doesn't wait.研究員在 GPU 排隊要等好幾天，科學不等人。"

Shared GPU cluster queues stretch experiment cycles three to five times. TB-scale training datasets hit storage throughput limits, and teams on PyTorch, JAX, and TensorFlow compete for the same resources. Grant-driven procurement and infrastructure lifecycle rhythms are completely out of sync. 共用 GPU cluster 排隊等候，實驗周期拖長 3–5 倍。TB 級訓練資料集受存取速度限制，PyTorch、JAX、TensorFlow 多團隊也互相搶資源。計畫型採購與 infrastructure lifecycle 的節奏完全脫節。