Deployment & Infrastructure

On-Premise LLM

An LLM deployed within an organization's own infrastructure (on-premises servers or private cloud), keeping model weights and all inference data under the organization's direct control.

Definition

An on-premise LLM is a large language model whose weights are downloaded and run entirely within infrastructure controlled by the deploying organization—either physical servers in their data center or virtual machines in a private cloud tenancy. Unlike API-based access to hosted models, on-premise deployment means that no inference data ever leaves the organization's environment, providing the highest level of data sovereignty and privacy control available for LLM deployments.

Engineering Context

On-premise LLMs are required when data sovereignty, privacy regulations (GDPR, HIPAA), or security policy prohibit sending data to external API providers. Leading on-premise options: Llama 3 (70B, 405B), Mistral (7B, 8x22B), Qwen 2.5, Gemma 3. Requires significant GPU infrastructure: a 70B parameter model in FP16 requires approximately 140GB VRAM. Model quantization (INT4/INT8) can reduce this to 40GB, enabling deployment on 2x A100 80GB GPUs. Serving frameworks: vLLM, TGI, and Ollama. Organizations must also manage model updates, security patching, and operational monitoring that cloud providers handle automatically with API-based LLMs.

Related Terms

Model Serving Inference Endpoint GPU Compute Model Quantization Data Privacy

Building production AI agents?

We design and implement deterministic AI agent systems for enterprise teams.

Start Assessment