私有 AI 与本地 LLM 部署
您的数据。您的模型。您的基础设施。
云端 AI API 要求把每条提示和回复都送到第三方服务器。对处理敏感知识产权、受监管数据或关键工作负载的机构，这种交换不可接受。私有 AI 彻底去掉它：模型跑在您拥有并控制的基础设施上。
您的数据。您的模型。您的基础设施。
云端 AI API 要求把每条提示和回复都送到第三方服务器。对处理敏感知识产权、受监管数据或关键工作负载的机构，这种交换不可接受。私有 AI 彻底去掉它：模型跑在您拥有并控制的基础设施上。
Organisations across regulated industries are moving AI workloads on-premise for four decisive reasons.
Prompts, embeddings, and completions never leave your network perimeter. You retain full ownership and auditability of every byte processed, eliminating third-party data-sharing agreements and residual-use clauses.
Meet HIPAA, GDPR, SOC 2, ITAR, and sector-specific mandates without relying on a vendor's compliance posture. On-premise deployment gives auditors a clear, controllable surface area.
Eliminate variable round-trip times to cloud endpoints. On-premise inference delivers consistent sub-100ms token latency, enabling real-time applications such as code completion, live chat, and process automation.
Cloud API costs scale linearly with token volume. On-premise hardware is a capital expense that depreciates while throughput increases through model optimisation. At sustained volume, self-hosted inference costs 60-80% less per token.
Understanding the trade-offs helps you choose the right approach for each workload.
| 维度 | 云端 API | 本地部署 |
|---|---|---|
| Data Residency | Data transits to provider data centres; subject to provider terms | Data never leaves your network; full custody and audit trail |
| Compliance | Dependent on vendor certifications and shared-responsibility models | You own the entire compliance surface; auditors inspect your controls |
| Latency | Variable; 100-500ms per request depending on load and region | Consistent sub-100ms; no network round-trip to external endpoints |
| Cost at Scale | Linear per-token pricing; unpredictable spikes during high usage | Fixed hardware cost amortised over time; 60-80% cheaper at volume |
| Model Customisation | Limited fine-tuning options; constrained by provider's supported models | Full control over fine-tuning, quantisation, and model selection |
| Availability | Subject to provider outages, rate limits, and deprecation schedules | Self-managed uptime with your own redundancy and failover |
| Setup Complexity | Minimal; API key and SDK integration | Requires hardware provisioning, model deployment, and operations |
The open-weight ecosystem now offers production-grade models across every capability tier.
提供方: Meta
Best general-purpose open model family. Strong reasoning, instruction following, and multilingual support. The 8B variant runs on a single consumer GPU; the 405B variant rivals frontier closed models.
最适合: General enterprise assistant, document analysis, code generation
提供方: Mistral AI
Exceptionally efficient inference at the 7B scale. Sliding-window attention enables long-context processing with modest memory. Mistral Large competes with GPT-4 class models.
最适合: High-throughput classification, summarisation, customer support
提供方: Mistral AI
Mixture-of-experts architecture activates only 2 of 8 expert networks per token, delivering 70B-level quality at 12B-level inference cost. Excellent reasoning and code capabilities.
最适合: Complex reasoning, multi-step analysis, code review
提供方: Your Organisation
LoRA and QLoRA adapters let you specialise any base model on your proprietary data in hours, not weeks. Domain-tuned models outperform general models on narrow tasks by 20-40%.
最适合: Medical coding, legal contract review, internal knowledge Q&A
Right-sizing your infrastructure depends on model size, concurrency, and latency targets.
Single NVIDIA RTX 4090 (24 GB VRAM) or Apple M-series Mac with 64 GB unified memory. Runs quantised 7-8B models at interactive speeds for small teams of 5-15 users.
支持的模型: Llama 3.1 8B, Mistral 7B, Phi-3
Dual NVIDIA A100 (80 GB each) or equivalent. Handles 70B-class models with 4-bit quantisation and supports 50-100 concurrent users with batched inference.
支持的模型: Llama 3.1 70B, Mixtral 8x7B, CodeLlama 70B
Multi-node clusters with 8x NVIDIA H100 GPUs per node. Required for full-precision 405B models and high-throughput production workloads serving thousands of users.
支持的模型: Llama 3.1 405B, Mixtral 8x22B, any model at scale
Certain sectors face regulatory, contractual, or operational constraints that make cloud AI impractical or prohibited.
HIPAA requires strict controls over Protected Health Information. On-premise AI enables clinical decision support, medical coding automation, and patient communication without exposing PHI to external processors.
SEC, FINRA, and PCI-DSS regulations demand auditable data handling. Private AI powers fraud detection, risk modelling, and client communication while keeping transaction data within the institution's perimeter.
Attorney-client privilege and confidentiality obligations prohibit sending case materials to third-party APIs. On-premise models enable contract analysis, legal research, and document review under full ethical compliance.
ITAR, FedRAMP, and classification requirements restrict data movement. Air-gapped deployments ensure AI capabilities are available in secure enclaves without any external network dependency.
Trade secrets, proprietary designs, and process data represent core competitive assets. Private AI enables predictive maintenance, quality analysis, and engineering assistance without IP exposure risk.
A production-grade private AI stack consists of layered components designed for reliability, observability, and scale.
GPU servers, high-bandwidth networking (NVLink/InfiniBand), shared storage (NFS/Ceph), Kubernetes or Docker orchestration
vLLM or TGI for optimised inference, model registry for version management, quantisation pipeline (GPTQ/AWQ/GGUF), automatic batching and scheduling
OpenAI-compatible REST API, authentication and rate limiting, request routing and load balancing, usage metering and chargeback
RAG pipelines with vector databases, agent frameworks with tool execution, prompt management and A/B testing, monitoring dashboards and alerting