CONCEPTUAL MODEL

Defining the boundaries of local intelligence.

0x01: What is "LLM On-Premise"?

Strictly defined, LLM On-Premise refers to the practice of executing Large Language Model inference entirely within infrastructure you control physically or legally (Private Cloud/VPC).

In this architecture, the model weights reside in your local memory (VRAM/RAM), and the computation happens on your silicon. Zero bits are sent to third-party API providers like OpenAI, Anthropic, or Google.

root@governance:~# check_sovereignty
> DATA_EGRESS: BLOCKED
> MODEL_OWNERSHIP: LOCAL
> STATUS: SOVEREIGN

The Stack Components

Inference Engine The runtime that loads weights and calculates tokens. (e.g., llama.cpp, vLLM, TGI).
Model Artifacts The static files containing neural network weights (e.g., .gguf, .safetensors).
Compute Layer The physical hardware executing matrix multiplications (GPU, NPU, CPU).
Context Window The "short-term memory" buffer residing in VRAM during execution.

What It Is NOT

✕ SaaS via VPN
Connecting to a private Azure OpenAI instance via VPN is Private Cloud, but relies on external model governance.
✕ "Hybrid" RAG
Storing documents locally but sending them as prompts to GPT-4 is NOT on-prem inference.
✕ Fine-Tuning API
Uploading data to a provider to fine-tune a model hosted by them.

Why This Distinction Matters

🔐 Data Gravity

For regulated industries (Healthcare, Finance, Defense), moving petabytes of sensitive data to the model is legally impossible. The model must move to the data.

📉 Cost Predictability

Exchanging OpEx (Token cost) for CapEx (Hardware). Heavy usage workloads often become cheaper on owned hardware over time.

⚡ Latency & Reliability

Removing the network round-trip and external API rate limits guarantees consistent throughput for critical automated agents.