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vLLM

The high-throughput open-source inference server for LLMs — uses PagedAttention and continuous batching to serve open-weights models at production rates on a single GPU.

Definition

What vLLM means in practice

vLLM is the open-source LLM inference server that has become the de facto choice for sovereign deployments. Its core innovations — PagedAttention (memory management for the KV cache borrowed from operating-system virtual memory) and continuous batching (new requests can join a batch mid-flight rather than waiting for the batch to complete) — give 3-5× the throughput of a naive transformers.generate loop. In MindMap deployments vLLM serves Llama 3.3 70B (quantised) on 2× H100, or Llama 3.3 8B on a single A100, with median time-to-first-token under 400ms at 30-50 concurrent users.

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The Sovereign AI Playbook.

Enterprise generative AI that runs on your servers.

  • 01Definition. The four non-negotiable properties of a sovereign deployment — and the three patterns that look sovereign but aren't.
  • 02Why now. Three forces that converged in 2026: regulator pressure, the open-weights capability gap closing, and unit economics flipping.
  • 03Reference architecture. The five-layer stack we deploy: applications → agent runtime → sovereign LLM → RAG + KB → data stores. With model and tooling choices for each.
  • 04The regulators. SAMA · RBI · UK ICO · EU AI Act · DPDP · HIPAA — what each requires and how it maps to architecture.
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Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.

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