In recent years, artificial intelligence has gone from being a futuristic concept to an everyday tool in companies of all sizes. Running large-scale language models (LLMs) on-premises has become an attractive alternative to cloud solutions, particularly because of data control and customization. However, one key question often goes unanswered: how much does it actually cost to run an on-premises LLM? Beyond the price of the hardware, the power consumption of a GPU during hours of inference or training can make a big difference in the operating budget. This article breaks down the actual costs in euros per million tokens, looking at everything from the hardware needed to the strategic decisions companies need to make when adopting this technology.
To understand the true cost, it is essential to measure GPU consumption in practical scenarios. Take an RTX 3090, for example, one of the most popular cards for running local models. In controlled tests, power consumption varies depending on the LLM model, batch size, and software optimization. A small model like the Llama 3.2 1B can draw around 150 watts during generation, while a larger one like the Mixtral 8x7B can go up to 350 watts. If the average price of electricity in Spain is around €0.15/kWh, the cost per million tokens can range from €0.02 to €0.12, depending on the model and the efficiency of the code. This is a significant saving compared to public APIs, which typically charge between €0.50 and €2 per million tokens. But the comparison is not so simple: you have to consider the cost of acquiring the hardware, maintenance and, above all, the value of development time.
From a business perspective, the decision to run an on-premises LLM is not just an economic one. It involves evaluating data sovereignty, latency, scalability, and the ability to integrate the model with proprietary systems. This is where it makes sense to have a technology partner that offers bespoke applications to adapt these models to specific workflows. An on-premises LLM can be the core of an internal assistant, a document analysis system, or a recommendation engine. However, for it to work efficiently and securely, enterprise AI is needed that not only implements the model, but also designs the data architecture, security layer, and user interface.
Electricity consumption is only one variable. Another critical factor is opportunity cost: the time an engineer spends installing and optimizing the on-premises environment versus using a managed service. For example, setting up an LLM with support for AI agents on a local GPU may require advanced knowledge of CUDA, Docker, and frameworks such as llama.cpp or text-generation-webui. Many companies find that even though the cost per token is low, the investment in development hours outweighs the initial savings. That's why more and more organizations are opting for a hybrid approach: they use on-premises models for sensitive or low-latency tasks, and turn to AWS and Azure cloud services to scale when demand increases. This combination allows both cost and flexibility to be optimized.
In addition to the energy cost, another aspect that is often overlooked is the environmental impact. Running a local LLM for 8 hours a day can generate emissions equivalent to those of a small electric car. Although it may seem minor, in a corporate context where sustainability is increasingly relevant, measuring and reducing the carbon footprint becomes a priority. Some companies are implementing efficiency strategies, such as using quantized models or scheduling inferences in off-peak hours of electricity. This is where process automation services can help schedule and manage these cycles intelligently, also leveraging power bi tools to monitor consumption in real-time and generate efficiency reports.
Cybersecurity is another fundamental pillar when managing local models. By avoiding sending sensitive data to external servers, you reduce the risk of leaks, but you also shift the responsibility of securing your on-premises environment. A misconfigured LLM can expose information through prompt injections or vulnerabilities in the inference layer. Therefore, it is advisable to integrate cybersecurity from the design, carrying out periodic audits and applying security patches. Companies such as Q2BSTUDIO offer pentesting services specialized in AI systems, ensuring that the model and its infrastructure are protected against internal and external threats.
Finally, the real cost of running an on-premises LLM is not limited to the electric bill or the price of the hardware. It includes team development, integration, security, maintenance, and training. The companies that take the best advantage of this technology are those that understand their needs and know how to combine it with external solutions. For example, an on-premise model can feed a business intelligence services system like Power BI, generating insights from internal data without relying on expensive APIs. Or it can serve as the basis for AI agents to automate repetitive tasks in a customer service department. In this scenario, the decision to run an on-premises LLM becomes a strategic investment, not just a cost savings.
In summary, measuring the cost in euros per million tokens is a useful exercise, but it should not be the only criterion. The real question is: what value does having an LLM operating locally bring to your business? If the answer includes privacy, personalization, and control, then this option is worth exploring. And to do this efficiently, having technology partners that offer custom software and AI consulting can make the difference between a project that consumes resources and one that generates real competitive advantages. Q2BSTUDIO, with his experience in application development, cloud and cybersecurity, is ready to accompany companies in this transition, ensuring that every euro invested in running a local LLM translates into tangible results.
