May 2025 • 12 min read
Enterprise AI Infrastructure in 2025: Private LLMs That Don’t Break in Production
Enterprise AI isn’t hard because models are smart. It’s hard because production is unforgiving: security, audits, cost, uptime, and scaling. Here’s the architecture that works in 2025.
enterprise
on-prem
vllm
observability
Principle: Treat LLMs like a critical internal service. You need an inference engine, a gateway, permissions, audit trails, and monitoring — not just a GPU box.
Why enterprises go on-prem in 2025
- Data privacy: sensitive inputs stay inside your environment.
- Compliance: enforce access controls, logging, and audit trails.
- Predictable cost: CAPEX beats runaway per-seat/per-token SaaS bills.
- Control: choose models, versions, and routing policies.
The reference architecture
A stable on-prem LLM platform has these components:
- Compute: GPUs + networking + fast storage
- Inference engine: vLLM / TGI / similar (batching, streaming, throughput)
- Orchestration: Docker/Kubernetes for deployment and scaling
- API layer: OpenAI-compatible endpoints + routing + auth
- Observability: latency, throughput, GPU util, token rate (Prometheus/Grafana/OpenTelemetry)
- UI layer: internal chat + RAG (Open WebUI, etc.)
Scaling the right way (queue-based signals)
Autoscaling LLM inference is not like scaling web apps. Queue size is one of the most sensitive indicators of load spikes and correlates to latency. A practical approach is to scale based on queue growth — not CPU utilization.
Hardware strategy: “AI hub” instead of AI everywhere
For many enterprises, the best starting point is an internal AI hub: 1–2 inference machines serving multiple teams behind a single access layer.
- Minisforum MS‑S1 MAX is positioned as a rack-mountable private AI server for 3–5 concurrent users per PC.
- Use 1–2 boxes as a central hub for R&D, strategy, analytics, or internal support teams.
Need enterprise-grade Project Infra?
We can design your on-prem LLM platform: inference, routing, RBAC, audit logs, RAG, and observability — then deploy it on hardware you own.
Sources referenced for architecture patterns: 2025 on-prem LLM architecture guidance (compute, inference engines, orchestration, API gateway/routing, and observability) and 2025 autoscaling best-practices emphasizing queue-based scaling.