On-Premise AI

Sovereign Generative AI — your data never leaves your perimeter.

For regulated, public-sector and data-sensitive workloads, sending prompts to a public API is a non-starter. I design and operate on-premise LLM platforms on your own GPUs that match cloud quality on the use cases that matter.

What the platform includes

  • Model selection & sizing — LLaMA 3, DeepSeek, Mistral, Qwen and domain-tuned variants matched to your workloads and GPU budget.
  • Inference serving — vLLM / TGI / Ollama with tensor parallelism, paged attention, speculative decoding, batching, autoscaling.
  • Embeddings & rerankers — local BGE / E5 / Cohere-class models for RAG and GraphRAG.
  • Hardening — air-gapped or restricted-egress deployment, IAM integration, SIEM / audit log shipping, key management.
  • Observability — OpenTelemetry traces, token & GPU cost dashboards, latency and error SLOs.
  • Fine-tuning & adapters — LoRA / QLoRA on your domain data, with eval gates before promotion.

Reference deployment

Kubernetes on-prem (or Azure Stack / sovereign cloud) · NVIDIA GPU operator · vLLM serving · pgvector + Neo4j for retrieval · MCP server tier · LangGraph orchestrator · Next.js / Power BI front-ends · GitOps with ArgoCD · Prometheus + Grafana + Loki.

Why teams choose on-prem with me

  • Data sovereignty — meets UAE, GCC, EU and sector-specific residency requirements.
  • Predictable cost — fixed GPU spend vs per-token API bills that scale with success.
  • Latency & control — co-located with your data and apps, no third-party rate limits.
  • Compatibility — same platform serves GraphRAG, MCP servers and agentic automation.