Inference in Local/On-Prem DCs
Many enterprises choose to run AI inference workloads inside their own local or on-premises data centers. This approach offers tighter control over data security, sovereignty, compliance, and cost predictability. Unlike hyperscale inference, which serves global APIs, on-prem inference is tailored to enterprise-specific datasets and business processes. Industries such as finance, healthcare, and government rely heavily on local inference for mission-critical applications.
Overview
- Purpose: Enable organizations to run inference workloads privately, often for compliance or cost reasons.
- Scale: Ranges from small server clusters to 10–20 MW enterprise AI halls.
- Characteristics: Controlled environments, enterprise integration, often hybrid (connected to cloud APIs).
- Comparison: Unlike hyperscale inference, on-prem inference prioritizes privacy and integration with enterprise IT over elastic global scaling.
Common Use Cases
- Healthcare: Medical imaging analysis, EHR data inference, HIPAA-compliant LLMs.
- Finance: Trading models, risk scoring, fraud detection inside sovereign DCs.
- Government: Classified inference on air-gapped systems.
- Manufacturing: Predictive maintenance and quality control in smart factories.
- Legal & Compliance: Document review, contract analysis, private AI copilots.
Bill of Materials (BOM)
| Domain | Examples | Role |
|---|---|---|
| Compute | NVIDIA A100/H100, AMD MI300, Intel Gaudi, on-prem GPU clusters | Accelerators sized to enterprise-scale inference |
| Networking | Enterprise LANs, InfiniBand for GPU clusters | Interconnect inference servers and enterprise IT |
| Storage | Enterprise SAN/NAS, NVMe storage arrays | Hold model weights and enterprise datasets |
| Frameworks | ONNX Runtime, Triton, Hugging Face Optimum | Enable optimized inference inside private DCs |
| Orchestration | VMware, Kubernetes, OpenShift | Integrate inference workloads with enterprise IT ops |
| Security | HSMs, zero-trust, sovereign key mgmt | Protect sensitive enterprise and regulated data |
Facility Alignment
| Deployment | Best-Fit Facilities | Also Runs In | Notes |
|---|---|---|---|
| Healthcare AI | Enterprise DCs | Hyperscale (via hybrid) | HIPAA requires local control for PHI |
| Financial Models | Enterprise DCs, Colo | Hybrid IT | Low-latency risk and fraud inference |
| Government / Classified | Air-gapped Gov DCs | None | Runs entirely inside sovereign networks |
| Industrial IoT | Enterprise DCs | Edge DCs | Factories running inference close to OT systems |
| Legal & Compliance | Enterprise DCs | Hybrid IT | Contract review and GRC AI copilots |
Key Challenges
- CapEx: GPUs and accelerators are expensive to deploy at scale.
- Utilization: Enterprises may underutilize GPU clusters compared to hyperscalers.
- Expertise: Running AI inference stacks requires specialized skills.
- Compliance: Local workloads must align with sector regulations (HIPAA, SOX, GDPR).
- Hybrid Complexity: Balancing on-prem inference with cloud APIs for overflow or specialty models.
- Energy & Cooling: Enterprise DCs must retrofit to handle GPU rack densities.
Notable Deployments
| Deployment | Operator | Scale | Notes |
|---|---|---|---|
| JP Morgan AI Risk Models | JP Morgan | Enterprise DCs | On-prem inference for credit/risk scoring |
| Epic EHR AI Assist | Epic Systems + Hospitals | Enterprise DCs | Inference on patient data under HIPAA |
| DoD AI Pilots | US Department of Defense | Air-gapped | Inference in classified government facilities |
| Siemens Factory AI | Siemens | Enterprise / Industrial DCs | Predictive maintenance and quality control |
| Legal AI Copilots | AmLaw 100 firms | Enterprise DCs | Private inference for contracts and discovery |
Future Outlook
- Hybrid AI: On-prem inference augmented by hyperscale overflow via secure APIs.
- Sovereign AI: Growth of private LLMs deployed in sovereign clouds or local DCs.
- ASIC Adoption: Enterprises adopting inference-specific ASICs (Groq, Tenstorrent, etc.) for efficiency.
- Digital Twins: Enterprises integrating inference into simulations and IoT twins.
- Sustainability: Push for efficient GPU cooling and carbon-aware inference scheduling.
FAQ
- Why run inference on-prem? For data security, compliance, sovereignty, and cost predictability.
- Which industries use on-prem inference most? Healthcare, finance, government, manufacturing, legal.
- How big are on-prem inference clusters? From small GPU racks to tens of MW for large enterprises.
- Is on-prem inference cheaper? Often yes for steady workloads; cloud remains better for bursty demand.
- What’s next? Enterprise adoption of sovereign AI stacks and hybrid inference architectures.