私有化人工智慧的迫切需求：從昂貴的專有語言模式轉向安全、經濟高效的企業基礎設施

The Private AI Imperative: Shifting from Proprietary LLMs to Secure, Cost-Effective Enterprise Infrastructure

出版日期: 2025年11月04日 | 出版商:

Mind Commerce | 英文 65 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

大規模語言模型 (LLM) 的快速普及及其部署挑戰，使當前的企業格局處於關鍵的十字路口。企業面臨的首要挑戰顯而易見：擺脫昂貴且依賴外部資源的專有 LLM 和雲端服務，建構安全、經濟且自主的私有化人工智慧基礎設施。

常見的 AI 外包模式存在許多風險，包括敏感企業資料外洩、模型更新缺乏控制、營運成本不可預測且不斷上漲，以及複雜的監管合規性問題。

本報告強調了企業內部建構 AI 基礎設施的策略必要性。內部運行 AI 意味著可以使用自身數據對規模更小、更專業的開源模型進行微調，從而顯著降低推理成本，徹底避免供應商鎖定，同時也能融入行業特定知識。

透過採用私有人工智慧方法，將人工智慧推理和模型管理更靠近數據，企業可以釋放生成式人工智慧的真正力量，同時確保數據隱私，完全掌控智慧財產權，並建立可持續、可預測的人工智慧經濟模型。這種轉型不僅是簡單的技術升級，更是保護企業資產和確保長期競爭優勢的根本性商業策略。

依賴專有生命週期管理（LLM）會帶來多方面的風險，損害企業的資料、成本和策略方向。這些風險源自於將企業的核心能力委託給第三方 "黑箱" 。

企業現在處於極其脆弱的境地。過度依賴昂貴的專有生命週期管理（LLM）和外部雲端服務不再是創新的途徑；它是一種複雜且高風險的責任結構，會不斷削弱企業的控制權、資料安全和財務穩定性。

本報告分析了從專有LLM（生命週期管理）轉向私有AI（人工智慧）方法的影響，探討了外包AI功能的風險、內部運作AI的優勢、案例研究以及企業採用策略。

企業人工智慧策略：依賴專有LLM和私人基礎設施
企業人工智慧策略中的控制、成本、效能和支持
企業混合LLM策略作為替代方案
混合LLM策略：融合兩者優勢的最佳架構
企業LLM實施的關鍵：RAG（搜尋增強生成）架構
RAG架構
RAG實施的主要企業效益
企業LLM治理與防護措施
LLM治理：企業管理策略
LLM防護措施：技術控制框架
企業實施的關鍵防護措施要素
快速管理與防護控制層
人工智慧閘道：提示與護欄編排
LLM 評估 (LLMOps) 和紅隊演練
LLM 評估：如何衡量可靠性和效能
評估最佳實踐
紅隊演練：壓力測試護欄
紅隊演練在 LLMOps 生命週期中的地位
建構全面的企業級生成式 AI 架構的考量因素
端對端的企業級生成式 AI 架構
LLMOps 的組織結構和持續交付管道 (CI/CD)
組織架構：建立跨職能協調
LLMOps 管道：持續整合/持續交付 (CI/CD)
滿足企業架構與營運需求
AI 的企業安全和隱私要求
合規性與資料主權
客製化、準確性和效率
高度監管產業中私有LLM的應用案例
金融與銀行業（監理與風險管理視角）
醫療保健（病人隱私和臨床應用視角）
支援企業級生成式AI的半導體供應商策略
AMD的策略：專注於SLM和企業級RAG
NVIDIA的策略：企業級全端供應商
超大規模雲端供應商（AWS、Google Cloud、Microsoft Azure）
生成式AI市場供應商策略比較分析

第1章企業生成AI基礎設施的3個範例

策略格局概述
主要策略發現與建議

第2章基礎層:晶片結構和效能經濟

NVIDIA：加速運算工廠（垂直整合）
Intel：成本競爭力與開放路徑
超大規模客製化晶片：內部優化與價格穩定性

第3章生態系統戰爭:軟體，RAG，開發商體驗

NVIDIA AI 企業版與 NIM 微服務：提供生產就緒性
Intel 企業 AI 開放平台 (OPEA)：標準化與模組化
雲端平台：管理選擇與無縫整合（模型市場）

第四章：企業採用策略比較分析

總擁有成本與效率比較：超越晶片價格的真實成本評估
廠商鎖定與策略彈性
治理、安全與資料主權

第五章：結論與策略建議：策略與基礎設施的協調

決策框架：為您的工作負載選擇最佳供應商模式
建構彈性多供應商生成式人工智慧策略

簡介目錄

Overview:

The current enterprise landscape is at a critical juncture, defined by the pervasive yet challenging adoption of Large Language Models (LLMs). The imperative is clear: organizations must pivot away from reliance on expensive, proprietary LLMs and third-party cloud services to establish a secure, cost-effective, and sovereign private AI infrastructure.

The prevailing model of outsourcing AI capabilities poses significant risks, including the exposure of sensitive corporate data, lack of control over model updates, unpredictable and escalating operational costs, and regulatory compliance headaches.

This report underscores the strategic necessity for enterprises to bring AI infrastructure in-house. This shift involves leveraging smaller, specialized, and open-source models that can be fine-tuned on private data, thereby offering superior domain expertise while dramatically reducing inference costs and eliminating vendor lock-in.

By adopting this private AI approach of moving AI inference and model management closer to the data, companies can unlock the full potential of generative AI, ensuring data privacy, maintaining complete intellectual property control, and achieving a sustainable, predictable economic model for their AI future. This transformation is not merely a technological upgrade but a fundamental business strategy that safeguards corporate assets and ensures long-term competitive advantage.

The dependence on proprietary LLMs introduces a constellation of significant, multifaceted risks that erode an enterprise's control over its data, costs, and strategic direction. These risks fundamentally stem from turning a mission-critical capability into a black-box service managed by a third-party vendor.

Enterprises are critically exposed. The widespread, seemingly unavoidable reliance on expensive, proprietary LLMs and third-party cloud services is not a path to innovation - it's a massive, multi-faceted liability that is actively eroding your company's control, data security, and financial stability.

The clock is running. Every API call that enterprises make to a vendor-managed black box is a transaction that exposes sensitive corporate IP, subjects you to unpredictable, escalating operational costs, and puts you at risk of catastrophic regulatory non-compliance (GDPR, HIPAA, data sovereignty laws). Enterprises are effectively donating invaluable private data to a competitor while signing away your strategic independence through inevitable vendor lock-in.

Purchase this essential report from Mind Commerce now to gain the blueprint for this critical transition and secure your enterprise's AI future.

Executive Summary

Enterprise AI Strategy: Dependence on Proprietary LLMs vs. Private Infrastructure
Control, Cost, Performance, and Support in Enterprise AI Strategy
Enterprise Hybrid LLM Strategy as an Option
The Hybrid LLM Strategy: Best-of-Both-Worlds Architecture
Retrieval-Augmented Generation (RAG) Architecture Essential for LLM in Enterprise
Retrieval-Augmented Generation (RAG) Architecture
Key Enterprise Benefits of Using RAG
Enterprise LLM Governance and Guardrails
LLM Governance: The Enterprise Strategy
LLM Guardrails: The Technical Controls
Critical Guardrails for Enterprise Deployment
Prompt Management and Guardrail Orchestration Layer
The AI Gateway: Orchestrating Prompts and Guardrails
LLM Evaluation (LLMOps) and Red Teaming
LLM Evaluation: Measuring Trustworthiness and Performance
Evaluation of Best Practices
Red Teaming: Stress-Testing the Guardrails
Red Teaming in the LLMOps Life Cycle
Considerations for a Full Enterprise Generative AI Architecture
End-to-End Enterprise Generative AI Architecture
Organizational Structure and Continuous Delivery Pipelines (CI/CD) for LLMOps
Organizational Structure: Cross-Functional Alignment
LLMOps Pipeline: Continuous Integration/Continuous Delivery (CI/CD)
Addressing the Architecture and Operational Needs for Enterprises
Enterprise Security and Privacy Imperatives for AI
Regulatory Compliance and Data Sovereignty
Customization, Accuracy, and Efficiency
Use cases for Private LLMs in a Highly Regulated Industries
Finance and Banking (Regulatory and Risk Management Focus)
Healthcare (Patient Privacy and Clinical Focus)
Chip Vendor Strategies supporting Enterprise Generative AI
AMD's Strategy for SLMs and Enterprise RAG
NVIDIA Strategy: A Full-Stack Provider for Enterprise
Hyperscale Cloud Providers (AWS, Google Cloud, Microsoft Azure)
Comparing Vendor Strategies in the Generative AI Landscape

1. The Three Paradigms of Enterprise GenAI Infrastructure

1.1. Strategic Landscape Overview
1.2. Key Strategic Findings & Recommendations

2. The Foundational Layer: Chip Architecture and Performance Economics

2.1. NVIDIA: The Accelerated Computing Factory (Vertical Integration)
2.2. Intel: The Cost-Competitive and Open Path
2.3. Hyperscale Custom Silicon: Internal Optimization and Pricing Stability

3. The Ecosystem War: Software, RAG, and Developer Experience

3.1. NVIDIA AI Enterprise and NIM Microservices: Selling Production Readiness
3.2. Intel's Open Platform for Enterprise AI (OPEA): Standardization and Modularity
3.3. Cloud Platforms: Managed Choice and Seamless Integration (The Model Marketplace)