How Much Does It Actually Cost to Run a Local LLM? (Euros per Million Tokens, Measured)
Local LLM inference is not inherently cheaper than cloud APIs; cost efficiency depends heavily on specific model sizes, quantization, and electricity tariffs. The author developed an open-source tool, HomeLab Monitor, to accurately measure real-time GPU power consumption via `nvidia-smi` rather than relying on TDP estimates. In a controlled benchmark on an RTX 3090, five out of eight tested models were cheaper to run locally than hosted "Flash"-class APIs, but three were more expensive. Cost cal
Analysis
TL;DR
- Local LLM inference is not inherently cheaper than cloud APIs; cost efficiency depends heavily on specific model sizes, quantization, and electricity tariffs.
- The author developed an open-source tool, HomeLab Monitor, to accurately measure real-time GPU power consumption via
nvidia-smirather than relying on TDP estimates. - In a controlled benchmark on an RTX 3090, five out of eight tested models were cheaper to run locally than hosted "Flash"-class APIs, but three were more expensive.
- Cost calculation requires integrating power usage over the exact inference window and applying dynamic electricity rates (day/night tariffs) to determine cost per million tokens.
Why It Matters
This analysis challenges the common assumption that running models locally is always cost-effective, providing empirical data for practitioners deciding between self-hosting and cloud APIs. It highlights the importance of precise energy measurement in evaluating the true operational costs of AI infrastructure, especially as energy prices fluctuate. For researchers and engineers, it offers a reproducible methodology for benchmarking local inference costs against commercial services.
Technical Details
- Hardware & Environment: Benchmark conducted on a single openSUSE machine equipped with an NVIDIA RTX 3090 (24 GB VRAM).
- Models Tested: Three Gemma variants served via Ollama, all using Q4_K_M-quantized GGUF weights:
gemma3:1b,gemma4:26b(~25.8B params), andgemma3:27b. - Workload: A sustained loop of 256-token generations cycling through five fixed prompts for approximately 4 minutes (240 seconds) to ensure the GPU reached a steady state.
- Measurement Methodology: Power sampled from
nvidia-smievery 10 seconds, integrated over the run's start-to-end window to calculate kWh. Costs were computed using specific Bulgarian electricity tariffs (0.30 BGN/day, 0.18 BGN/night) and converted to EUR. - Tooling: An open-source, MIT-licensed dashboard called HomeLab Monitor was used to track runs, integrate power data, and calculate costs, with a Python client library provided for reproducibility.
Industry Insight
- Cost Transparency is Critical: Organizations should implement precise energy monitoring for local AI deployments, as "free" local inference often ignores significant electricity costs that can exceed cloud API fees for larger models.
- Model Selection Strategy: Smaller, quantized models may offer better cost-efficiency locally, but practitioners must benchmark specific workloads against their local energy rates before committing to self-hosting.
- Reproducible Benchmarks: The availability of open-source tools for measuring inference costs encourages standardization in AI performance evaluation, moving beyond latency/accuracy metrics to include economic sustainability.
Disclaimer: The above content is generated by AI and is for reference only.