私有化人工智慧的迫切需求：从昂贵的专有语言模式转向安全、经济高效的企业基础设施

大规模语言模型 (LLM) 的快速普及及其部署挑战，使当前的企业格局处于关键的十字路口。企业面临的首要挑战显而易见：摆脱昂贵且依赖外部资源的专有 LLM 和云端服务，建构安全、经济且自主的私有化人工智慧基础设施。

常见的 AI 外包模式存在许多风险，包括敏感企业资料外洩、模型更新缺乏控制、营运成本不可预测且不断上涨，以及复杂的监管合规性问题。

本报告强调了企业内部建构 AI 基础设施的策略必要性。内部运行 AI 意味着可以使用自身数据对规模更小、更专业的开源模型进行微调，从而显着降低推理成本，彻底避免供应商锁定，同时也能融入行业特定知识。

透过采用私有人工智慧方法，将人工智慧推理和模型管理更靠近数据，企业可以释放生成式人工智慧的真正力量，同时确保数据隐私，完全掌控智慧财产权，并建立可持续、可预测的人工智慧经济模型。这种转型不仅是简单的技术升级，更是保护企业资产和确保长期竞争优势的根本性商业策略。

依赖专有生命週期管理（LLM）会带来多方面的风险，损害企业的资料、成本和策略方向。这些风险源自于将企业的核心能力委託给第三方 "黑箱" 。

企业现在处于极其脆弱的境地。过度依赖昂贵的专有生命週期管理（LLM）和外部云端服务不再是创新的途径；它是一种复杂且高风险的责任结构，会不断削弱企业的控制权、资料安全和财务稳定性。

本报告分析了从专有LLM（生命週期管理）转向私有AI（人工智慧）方法的影响，探讨了外包AI功能的风险、内部运作AI的优势、案例研究以及企业采用策略。

目录

摘要整理

• 企业人工智慧策略：依赖专有LLM和私人基础设施
• 企业人工智慧策略中的控制、成本、效能和支持
• 企业混合LLM策略作为替代方案
• 混合LLM策略：融合两者优势的最佳架构
• 企业LLM实施的关键：RAG（搜寻增强生成）架构
• RAG架构
• RAG实施的主要企业效益
• 企业LLM治理与防护措施
• LLM治理：企业管理策略
• LLM防护措施：技术控制框架
• 企业实施的关键防护措施要素
• 快速管理与防护控制层
• 人工智慧闸道：提示与护栏编排
• LLM 评估 (LLMOps) 和红队演练
• LLM 评估：如何衡量可靠性和效能
• 评估最佳实践
• 红队演练：压力测试护栏
• 红队演练在 LLMOps 生命週期中的地位
• 建构全面的企业级生成式 AI 架构的考量因素
• 端对端的企业级生成式 AI 架构
• LLMOps 的组织结构和持续交付管道 (CI/CD)
• 组织架构：建立跨职能协调
• LLMOps 管道：持续整合/持续交付 (CI/CD)
• 满足企业架构与营运需求
• AI 的企业安全和隐私要求
• 合规性与资料主权
• 客製化、准确性和效率
• 高度监管产业中私有LLM的应用案例
• 金融与银行业（监理与风险管理视角）
• 医疗保健（病人隐私和临床应用视角）
• 支援企业级生成式AI的半导体供应商策略
• AMD的策略：专注于SLM和企业级RAG
• NVIDIA的策略：企业级全端供应商
• 超大规模云端供应商（AWS、Google Cloud、Microsoft Azure）
• 生成式AI市场供应商策略比较分析

第1章 企业生成AI基础设施的3个范例

• 策略格局概述
• 主要策略发现与建议

第2章 基础层:晶片结构和效能经济

• NVIDIA：加速运算工厂（垂直整合）
• Intel：成本竞争力与开放路径
• 超大规模客製化晶片：内部优化与价格稳定性

第3章 生态系统战争:软体，RAG，开发商体验

• NVIDIA AI 企业版与 NIM 微服务：提供生产就绪性
• Intel 企业 AI 开放平台 (OPEA)：标准化与模组化
• 云端平台：管理选择与无缝整合（模型市场）

第四章：企业采用策略比较分析

• 总拥有成本与效率比较：超越晶片价格的真实成本评估
• 厂商锁定与策略弹性
• 治理、安全与资料主权

第五章：结论与策略建议：策略与基础设施的协调

• 决策框架：为您的工作负载选择最佳供应商模式
• 建构弹性多供应商生成式人工智慧策略

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.

Table of Contents

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)

4. Comparative Strategic Analysis for Enterprise Adoption

• 4.1. TCO and Efficiency Comparison: Beyond the Chip Price
• 4.2. Vendor Lock-in and Strategic Flexibility
• 4.3. Governance, Security, and Data Sovereignty

5. Conclusions and Strategic Recommendations: Aligning Strategy with Infrastructure

• 5.1. Decision Framework: Matching Workload to Vendor Paradigm
• 5.2. Building a Resilient, Multi-Vendor GenAI Strategy