Table of Contents
For teams committing to self-hosted AI, the first weeks set the trajectory. Standard sequence: provision → deploy → eval → production. Don't skip steps; don't over-engineer. ~4 weeks to production-grade.
Week 1: provision GPU, install vLLM, get a test workload running. Week 2: build eval harness with 100-200 representative prompts. Week 3: integrate with your application via OpenAI-compatible API. Week 4: production deploy with observability + nginx + auth + monitoring. ~4 weeks for production-ready self-hosted AI.
Week one
- Day 1-2: provision dedicated GPU (5060 Ti for SMB; 4090 for mid-market). Verify drivers + CUDA.
- Day 3-4: install vLLM; serve a model (Llama 3.1 8B FP8 is the safe default); verify with curl + sample prompts.
- Day 5: run benchmark sweep (your prompts at expected concurrency); confirm capacity.
Week two-three
- Build eval harness: 100-200 representative prompts + grading rubric (LLM-as-judge or manual)
- Run eval against vLLM; baseline scores
- Run same eval against hosted API for quality comparison
- Document gap; identify hardest 5-10% for fallback routing
- Prepare LiteLLM router config for hybrid (vLLM primary + hosted API fallback)
Week four
- Front vLLM with nginx (TLS + auth + rate limit)
- Set up DCGM Exporter + Prometheus + Grafana
- Configure structured JSON logging
- Run soak test (48 hours synthetic load)
- Cut over with feature flag: 5% → 25% → 100% over a few days
- Monitor closely first 2 weeks; iterate based on production signals
Verdict
~4 weeks of focused work takes a team from "considering self-hosted" to production-grade dedicated GPU AI. The sequence above hits the essentials without over-engineering. After production launch: continuous improvement via eval + monitoring. Self-hosted AI is genuinely accessible in 2026.
Bottom line
4 weeks to production. See deployment checklist.
