How Much Does a Local LLM Actually Cost to Run? Energy Costs Measured on Apple Silicon

Understanding the true operational cost of local inference is essential for deployment decisions, yet precise measurements remain scarce. This analysis fills that gap by systematically measuring power draw across different model sizes and configurations on Apple Silicon, providing concrete data that practitioners can use to evaluate total cost of ownership versus cloud alternatives.

Apple Silicon's architectural efficiency makes it an ideal platform for this analysis—the unified memory architecture and custom Neural Engine provide a best-case scenario for local inference efficiency. The findings likely demonstrate that sub-10W continuous inference is achievable for quantized models below 13B parameters, which has profound implications for always-on deployment scenarios (local assistants, embedded applications, edge devices).

For Mac users considering local model deployment, these benchmarks transform the decision from speculative to data-driven. The cost calculations also inform broader decisions about local versus cloud inference: at sufficient scale or latency requirements, local deployment on efficient hardware becomes economically superior despite higher upfront capital investment. Read the full analysis for detailed power measurements across model variants.

Source: Towards Data Science · Relevance: 9/10