How to deploy a private LLM — A step-by-step guide

A private LLM, deployed correctly, is a production service like any other. The difference is the model: it is yours, it runs in your infrastructure, and the data flowing through it stays inside your perimeter. Here is the playbook we use on every engagement.

Step 1: Choose your model

Benchmark Llama 3.x, Mistral, Qwen, and DeepSeek on your task. Don't pick the biggest model; pick the smallest one that hits your quality target. Latency, GPU budget, and context length are the constraints. Run a small eval suite on five to ten candidate models before you commit.

Step 2: Size your GPU infrastructure

Calculate VRAM (model weights + KV cache + activations) and target throughput (tokens per second). Pick A100, H100, or MI300X based on availability in your region. Provision in the cloud region that matches your data residency requirements: AWS Mumbai for India DPDP, ca-central-1 for OSFI, eu-west-1 for GDPR.

Step 3: Deploy with vLLM or TGI

Containerize the serving runtime. Configure autoscaling, request queueing, KV-cache settings, and continuous batching for your workload. vLLM is the default choice in late 2025; TGI is a fine alternative for Hugging Face shops.

Step 4: Wire in auth and observability

The model endpoint is just another internal service. Put it behind your existing identity provider for request auth. Send logs, traces, and metrics to your observability stack. The on-call team should be able to debug an LLM the same way they debug a Postgres database.

Step 5: Run evals on every update

Build a task-specific evaluation pipeline before you ship. Run it on every model update, prompt change, and infrastructure change. Quality regressions get caught before production, not after a customer notices.

What this delivers

A production-grade LLM serving infrastructure inside your perimeter. Zero data egress. Predictable costs. The ability to fine-tune on your data when you need to. The ability to swap models when better ones arrive. Total ownership.