Phison AI Data Platform Phison AI Data Platform

"Our GPUs sit at 35% utilization between jobs, but we still can't serve more users.我們的 GPU 在任務間閒置率達 35%，但我們仍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 耗盡導致 OOM 與並發請求數硬上限。多個推論實例無法共享 KV Cache 池，造成冗餘運算浪費。Checkpoint 載入時 Storage I/O 成為瓶頸，且難以跨模型動態分配 GPU 資源。

"Managing separate GPU racks, storage arrays, and switches is eating my team alive.管理獨立的 GPU 機架、儲存陣列與交換器，正把我的團隊逼到極限。"

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. 三套獨立系統——運算、儲存與網路——各需專屬維運團隊。AI 工作負載的功率密度打破傳統資料中心規劃，6–9 個月的採購週期跟不上業務擴張。容量規劃同樣棘手：over-provision 浪費預算，under-provision 則違反 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 在量產規模下超出預算，且敏感資料不能出境——因此 OpenAI 與 Azure 並不可行。缺乏內部 GPU 基礎設施工程師，外包則面臨廠商鎖定風險，也不清楚哪款開源 LLM 適合業務需求。

"Our compliance team vetoed cloud AI. We're stuck running nothing.我們的合規團隊否決了雲端 AI，我們陷入停擺。"

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. 法規要求在 GDPR 與醫療隱私法下於本地執行推論。每次 AI 推論都需完整稽核日誌，公有雲無法提供；含敏感客戶資訊的訓練資料亦不得流向外部服務。通過 ISO 27001 / SOC 2 稽核，使外部 AI 服務更難獲批。

"Our researchers wait days in GPU queues. Science doesn't wait.研究人員在 GPU 佇列中等待數天，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. 共享 GPU 叢集佇列使實驗週期延長 3–5 倍。TB 級訓練資料集觸及儲存吞吐上限，PyTorch、JAX 與 TensorFlow 各團隊競爭相同資源。以補助為導向的採購節奏與基礎設施生命週期完全脫節。

"Our GPUs sit at 35% utilization between jobs, but we still can't serve more users.我們的 GPU 在任務間閒置率達 35%，但我們仍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 耗盡導致 OOM 與並發請求數硬上限。多個推論實例無法共享 KV Cache 池，造成冗餘運算浪費。Checkpoint 載入時 Storage I/O 成為瓶頸，且難以跨模型動態分配 GPU 資源。

"Managing separate GPU racks, storage arrays, and switches is eating my team alive.管理獨立的 GPU 機架、儲存陣列與交換器，正把我的團隊逼到極限。"

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. 三套獨立系統——運算、儲存與網路——各需專屬維運團隊。AI 工作負載的功率密度打破傳統資料中心規劃，6–9 個月的採購週期跟不上業務擴張。容量規劃同樣棘手：over-provision 浪費預算，under-provision 則違反 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 在量產規模下超出預算，且敏感資料不能出境——因此 OpenAI 與 Azure 並不可行。缺乏內部 GPU 基礎設施工程師，外包則面臨廠商鎖定風險，也不清楚哪款開源 LLM 適合業務需求。

"Our compliance team vetoed cloud AI. We're stuck running nothing.我們的合規團隊否決了雲端 AI，我們陷入停擺。"

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. 法規要求在 GDPR 與醫療隱私法下於本地執行推論。每次 AI 推論都需完整稽核日誌，公有雲無法提供；含敏感客戶資訊的訓練資料亦不得流向外部服務。通過 ISO 27001 / SOC 2 稽核，使外部 AI 服務更難獲批。

"Our researchers wait days in GPU queues. Science doesn't wait.研究人員在 GPU 佇列中等待數天，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. 共享 GPU 叢集佇列使實驗週期延長 3–5 倍。TB 級訓練資料集觸及儲存吞吐上限，PyTorch、JAX 與 TensorFlow 各團隊競爭相同資源。以補助為導向的採購節奏與基礎設施生命週期完全脫節。