The Real Cost of Running LLMs On-Premise (It's Not What You Think)

Description and use

Migrating LLM inference to a local GPU cut monthly costs to $58–73, but required 40 hours of debugging and improved data privacy.

Problem

High cloud inference bills and lack of privacy prompted a move to on‑prem GPU inference.

Outcome

On‑prem inference reduced monthly spend by $107–180 after 3–4 months, provided full data privacy, and yielded deep operational knowledge, but required 40 hours of debugging and a RAM upgrade.

Workflow

• 1. Purchase mid‑range GPU and upgrade host RAM
• 2. Install CUDA and Docker, set up Ollama
• 3. Resolve driver and networking issues (4+ hours)
• 4. Download and run 14B model
• 5. Build and run inference pipelines
• 6. Monitor power usage and performance
• 7. Iterate on RAM and model configuration

Evidence

$58–73 per month · incl. hardware amortization

Source

$180–240 per month · frontier API model at this volume

Source

forty hours of my life in the first three months that had nothing to do with building AI systems

Source