What Are the Biggest Costs of Running a Large Language Model Locally?

Running a large language model (LLM) locally can be appealing for some organizations due to heightened control, data privacy, and potential long-term cost benefits at extreme scale. However, without relying on flexible, pay-as-you-go cloud services, the expenses primarily fall into significant upfront capital expenditure (CapEx) and continuous operational expenditure (OpEx), encompassing hardware, electricity, maintenance, and expert operational staff. This article breaks down the main costs involved in running an LLM locally, including estimated dollar amounts where available.

Hardware Costs

The first and most obvious expense is purchasing the right hardware. Large language models require powerful computing equipment, typically involving multiple Graphics Processing Units (GPUs) or specialized processors (like TPUs).

High-end GPUs or TPUs used for training and inference can cost tens of thousands of dollars each. For instance, an NVIDIA A100 GPU can range from \$8,000 to \$20,000+ depending on the model (40GB PCIe to 80GB SXM). The newer NVIDIA H100 GPU is even more expensive, typically starting around \$25,000 to \$40,000+ per unit, with some configurations exceeding this. Scaling up to the dozens, hundreds, or even thousands of GPU units needed for running massive models or training new ones from scratch can mean hundreds of thousands, if not millions of dollars, just to acquire the core processing hardware. For example, a full H100 GPU system with multiple chips can cost up to \$400,000 or more.

Additionally, supporting infrastructure like servers, high-speed storage units, specialized network switches, and cooling systems add further costs. These can easily account for an additional 10-30% of the GPU cost.

It's not just about initial purchase. Hardware must be upgraded over time to keep up with advances in LLM architecture and performance, which means ongoing capital expenses within a few years. Hardware components also have a limited lifespan and may need replacement or upgrades every few years, often incurring substantial costs for individual components.

Electricity and Power Costs

Running powerful GPUs consumes large amounts of electricity. A single NVIDIA H100 GPU, for example, can draw up to 700W. A data center or server room housing these machines needs reliable power supplies, often with backup generators for outages, increasing the complexity and cost of electrical infrastructure.

Electricity costs vary based on location but are generally substantial. Data center electricity costs can range from \$0.05 to \$0.30 per kilowatt-hour (kWh). For a modest setup with 8 H100 GPUs continuously running (consuming roughly 5.6 kW), the annual electricity bill would be approximately \$7,358 (at \$0.15/kWh), not including cooling. For larger deployments, with dozens or hundreds of GPUs, these costs can quickly escalate into tens or hundreds of thousands of dollars annually. The continuous operation of multiple GPUs running at high capacity can lead to monthly power bills that are a significant part of the ongoing expenses. Cooling this hardware efficiently to prevent overheating further increases electricity costs, as data centers often require extensive cooling systems which themselves consume significant power.

Failure to adequately power and cool hardware results in overheating, hardware malfunction, or failure. Therefore, these energy expenses are unavoidable and can surpass initial hardware capital costs over time. Power is almost always the main operating cost of a data center.

Maintenance and Hardware Failures

Hardware components are prone to failure, especially under heavy workloads and continuous operation. Maintaining a fleet of high-performance GPUs and servers involves routine checks, proactive repairs, and eventual replacements. This typically requires a budget for replacement parts, which can be 5-10% of the initial hardware cost annually.

Employees with specialized skills are necessary for hardware maintenance. This staff ensures hardware uptime and handles troubleshooting, repairs, and upgrades. When hardware fails, replacements and repairs are also costly, both in terms of parts and the specialized labor required.

Keeping hardware in optimal condition demands substantial time and money, especially in a large-scale setup. Downtime caused by hardware failures can also result in lost productivity or data loss, leading to indirect costs.

Software and Licensing Fees

While some open-source models (like those from Hugging Face) and development frameworks (like PyTorch or TensorFlow) are free, companies often need to pay for proprietary tools, frameworks, or optimized software that enhances performance, security, or manageability. These can include:

• Enterprise MLOps (Machine Learning Operations) platforms for managing the ML lifecycle.
• Specialized operating system licenses (e.g., enterprise Linux distributions).
• Database licenses for storing data and model artifacts.
• Security software and monitoring tools.
• Commercial support for open-source software.

Annual software and licensing fees can range from tens of thousands of dollars to hundreds of thousands of dollars annually, depending on the scale and complexity of the deployment. In addition, software updates, security patches, and custom tuning also add to operational costs. Maintaining a secure and up-to-date system is crucial but costly in terms of software licenses and management.

Storage Costs

LLMs generate and require massive amounts of data for both training and operation. Model weights and training datasets can easily span hundreds of gigabytes to several petabytes (PB). Storage hardware capable of handling such data is expensive, particularly if high-speed, reliable, and redundant storage solutions are desired to reduce latency and prevent data loss.

A petabyte of on-premise storage could cost over \$1 million over five years. While raw disk costs can be lower (\$0.06/GB for SSDs or \$0.01/GB for mechanical drives in bulk, petabyte range), the overall system cost including servers, redundant arrays, networking, and backup solutions significantly drives up the price. Backup systems, redundancy, and disaster recovery plans all add to this expense. Over time, data storage needs grow, necessitating expansion of storage hardware.

Cooling and Physical Space

Large-scale hardware setups need dedicated physical space that can accommodate equipment and cooling infrastructure. This space must be adequately cooled and ventilated, which incurs costs such as specialized cooling systems (e.g., liquid cooling), raised flooring, enhanced fire suppression systems, and robust physical security.

The cost of leasing data center space (colocation) can be substantial, with average national asking prices in North America reaching \$163.44 per kW/month in 2023. For a compute footprint drawing 50kW (e.g., around 100 GPUs), this could amount to \$8,172 per month or nearly \$98,000 annually just for space and power connection, before factoring in actual electricity consumed. Building a new data center or expanding an existing one can involve multi-million dollar investments, with estimates around \$11,500 to \$25,000 per kilowatt of capacity. Facilities often need to be located in areas with favorable climate conditions to reduce cooling costs, but this can limit choices and increase real estate expenses.

Operational Staff

Running a large language model requires a highly specialized team. Data engineers, system administrators, security specialists, and machine learning research scientists and engineers are needed to manage, monitor, and optimize the system.

Salaries for these highly skilled staff make up a significant portion of the total ongoing costs. In the US, average annual salaries can range:

• Machine Learning Engineers: Average of \$158,147 to \$249,000+ annually, with senior roles exceeding \$300,000.
• DevOps/System Administrators: Average of \$88,927 to \$164,012+ annually, depending on experience and certifications.
• Security Specialists: Average \$158,594+ annually (for certified professionals).

A minimal team of even 3-5 such specialists could easily contribute \$400,000 to over \$1,000,000 annually in salary expenses alone. They handle everything from hardware upkeep and software tuning to security, data governance, and model optimization. The scarcity of these skills further drives up compensation.

Opportunity Cost

Beyond direct monetary outlays, there's a significant opportunity cost. The time and resources spent on procuring, setting up, maintaining, and troubleshooting complex on-premise infrastructure are resources that are not being directed towards core business activities, innovation, or model development. This can slow down development cycles and time-to-market for LLM-powered applications.

Operating a large language model locally without cloud solutions involves high initial investments in hardware and physical infrastructure, paired with substantial ongoing operational costs for power, cooling, maintenance, software, and highly specialized personnel. Organizations need to carefully weigh these extensive expenses, which can quickly sum up to millions of dollars in CapEx and hundreds of thousands to millions annually in OpEx, against the benefits of control and data privacy before deciding to run large models entirely in-house. While potentially offering cost advantages at extreme, sustained scale for certain organizations, for many, the flexibility, immediate scalability, and managed services of cloud providers often present a more financially viable and operationally less burdensome alternative.