Running large language models locally using the Stratat engine often costs less than cloud API usage only when you run significant volumes of inference. For sporadic use, the high upfront hardware cost and electricity expenses may make cloud APIs more economical. You must measure your own hardware efficiency to determine the break-even point.
Measuring Your Energy Consumption
To understand the true cost, you must monitor your system's power draw while the Stratat client is active. Use your operating system's built-in resource monitor or a hardware tool to track wattage. This data allows you to calculate the kilowatt-hours (kWh) required for your specific task. Multiply this usage by your local electricity rate of $0.17 per kWh to find the operational cost.
Comparing Against Cloud Pricing
Cloud providers charge per token or per minute. You need to estimate how many tokens you generate in a typical session. If your local electricity cost per token is lower than the cloud provider's rate, you save money. However, you must also account for the hardware depreciation. Stratat allows you to run these models without recurring monthly fees, but the hardware must last.
The Cost Equation
The following table illustrates how energy usage impacts the final cost comparison. These figures are derived from standard local inference workloads.
| Component | Metric | Cost Impact |
|---|---|---|
| GPU Power Draw | 250 Watts | $0.042 per hour at $0.17/kWh |
| Cloud API Fee | $0.03 per 1,000 tokens | Variable based on volume |
| Hardware Depreciation | $1,500 initial investment | Amortized over 3-5 years |
Defining the Core Term
Local inference refers to the process of running artificial intelligence models directly on your own hardware rather than sending data to a remote server. This approach ensures data privacy and eliminates recurring subscription fees for high-volume users.