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On-Prem AI Platform Architecture
Use the primary page first for the commercial and architectural overview, then move into the supporting articles for deeper implementation detail.
On-Prem AI Platform ArchitectureAI Topic Archive
On-Prem AI Architecture
Reference architecture, workload boundaries, and platform blueprint decisions for enterprise private AI environments.
Supporting articles
Building Document Understanding Pipelines with On-Premises Small Language Models
A practical guide to constructing document understanding pipelines using small language models on-premises, covering OCR integration, layout analysis, entity extraction, and classification workflows.
GPU Memory Management and KV Cache Optimization for On-Premises LLM Serving
Practical strategies for managing GPU memory and optimizing KV cache allocation when serving large language models on-premises, from paged attention to dynamic memory pooling.
Multi-Region On-Premises AI Deployment: Synchronizing Models Across Data Centers
How to deploy and synchronize AI models across geographically distributed on-premises data centers while maintaining consistency, low latency, and compliance with regional data regulations.
AI Data Security and Privacy On-Premises: A European Architecture Guide
How to design on-prem AI for GDPR, data residency, access control, and auditable privacy in European enterprise environments.
Latest Design Principles for Enterprise AI Systems
Modern design principles for enterprise AI systems that need to stay governable, composable, and useful in production.
How to Overcome Local (On-Premises) LLM Performance Problems
Why Local LLMs Struggle With Performance Deploying large language models (LLMs) on-premises—within your own servers or private cloud—has become an increasingly popular approach for organizations prioritizing: Data security & compliance Full control over infrastructure Customization of model behavior However, this control comes at a price:performance bottlenecks. Common challenges include: High inference latency: Slow response times […]