LLM On-Premise – Deploy AI Locally
> SYSTEM STATUS: ONLINE
On-premise solutions, server configurations, GPU workstations, and infrastructure to deploy and manage Large Language Models locally. Sovereignty starts here.
LLM On-Premise means running language-model inference entirely on infrastructure you control — the model weights live in your VRAM, the computation happens on your silicon, and zero bits reach a third-party API. It became practical when three things converged: genuinely capable open-weight models (Llama, Qwen, Mistral, Gemma), 4-bit quantization that shrank them onto single GPUs, and mature runtimes (Ollama, vLLM) that made serving them routine. Full conceptual model →
This observatory is the decision-support layer: it exists for the engineer sizing a GPU server, the architect weighing on-prem against an API, and the compliance owner mapping the EU AI Act onto a self-hosted stack. The material is organized as a path:
- Should this workload run locally? → Decision Axes and the deployment comparison
- On what hardware? → Hardware Matrix and Model Cards
- In what shape? → Reference Architectures and Checklists
- Under what rules? → Governance and EU AI Act
For long-form evergreen references — GPU buying, real TCO math, quantization, building a private ChatGPT — see the AI-Radar guides.
> DECISION_SUPPORT_MATRIX
Constraint-based decision frameworks for deployment planning
Compare On-Premise, Hybrid, and API-Only deployment models across 5 decision axes.
ACCESS MATRIX →Industry-specific deployment scenarios with weighted constraints and failure modes.
Standardized deployment patterns with scenario fit analysis and implementation constraints.
Scenario-specific pre-deployment verification checklists. Manufacturing (uptime, edge), Pharma (21 CFR Part 11 validation), Enterprise IT (security, scalability). Verification gates, not recommendations.
VIEW CHECKLISTS →Constraint-focused decision reasoning engine for deployment planning questions.
QUERY SYSTEM →Curated cards for Llama 3.3 70B, Qwen3.6 27B, Mistral Small 3.1, Phi-4, Gemma 3 27B, DeepSeek-R1 32B — VRAM, license, and hardware tier.
BROWSE MODELS →Run LLM agents locally: LangGraph vs AutoGen vs CrewAI, tool sandboxing, persistent memory, token budgets, and security guardrails.
AGENT GUIDE →Mixture of Experts on consumer hardware: active vs total params, VRAM implications, quantization selection, and failure modes for Qwen3.6-35B-A3.7B and Mixtral.
MOE GUIDE →EU AI Act timeline, risk classification, high-risk obligations (Aug 2026 ⚡), and how on-premise deployment simplifies regulatory compliance.
COMPLIANCE GUIDE →> BENCHMARK_METRICS
2026 target configurations — Blackwell & Ada Lovelace
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