These days, there’s a growing debate about whether it’s feasible—or even wise—to upload large datasets directly to Large Language Models (LLMs). People often think: “Why not gather all the data and just feed it to the model?” However, our recent findings suggest that even six months of data can consume anywhere from 300,000 to 500,000 tokens, depending on the dataset type. (In this context, we are specifically talking about Green Button energy data.)

When you give an LLM such an enormous volume of information, two key problems arise: privacy and token usage. First, you risk exposing all your proprietary data to the LLM—especially if you rely on closed-loop, external models where you have little control over data handling. Second, as the token count increases significantly, you face practical limits on how much data the model can ingest at once, along with escalating costs.

Why Uploading Huge Datasets Can Be Risky

In the current market, open-source models don’t always achieve the same performance levels as proprietary, closed-loop solutions. But using a closed-loop model often entails sending large swaths of information to external servers. For many industries—particularly those dealing with regulated data—this raises serious concerns about data exposure and compliance.

Another critical issue is cost. The more tokens you feed into an LLM, the higher your expenses grow. At a certain scale, these costs can exponentially increase, making it unsustainable for large utilities or enterprises that handle massive data volumes.

The Local Processing Advantage

Rather than uploading entire datasets directly to an LLM, we advocate a local processing approach. In this method, your data undergoes initial transformations and analysis within your own secure environment. Only the distilled or summarized parts—those truly critical to your query—are sent to the LLM. This setup provides several benefits:

• Reduced Token Usage: By parsing and refining data locally, you dramatically cut down on the number of tokens being sent to the LLM, keeping usage (and costs) in check.
• Preserved Privacy: Sensitive or confidential information never leaves your premises, minimizing the risk of data leaks or unauthorized access.
• Controlled Costs: Uploading fewer tokens means paying less for model interactions—a crucial factor for large utilities or enterprises processing data at scale.

Thanks to local processing, you can maintain a strong layer of security and reduce dependencies on external systems. Once the essential data is extracted, LLMs can still provide robust analytical insights, summarizations, and recommendations without requiring the entire dataset at once.

Scalability and Practicality

In a large utility scenario, the volume of hourly or even real-time energy consumption data can be staggering. If you tried to feed several years of this data directly into a model, you’d hit token limits in no time—and the costs might be prohibitively high. But by processing data locally, you can manage complexity effectively:

• Incremental Summaries: Generate daily or weekly summaries before sending them to the LLM.
• Focused Queries: Instead of uploading everything, narrow down to specific questions or metrics, ensuring only relevant chunks go to the model.
• Ongoing Insights: As new data arrives, you can continuously integrate it at the local layer and only consult the model when you need a fresh interpretation or insight.

Conclusion

The discussion around uploading massive datasets to LLMs often overlooks practical hurdles—ranging from privacy to skyrocketing token usage. While direct ingestion of large datasets may appear convenient, it can lead to serious security risks and rising costs as token counts grow.

By adopting a local processing strategy, organizations gain better control over their data, maintain strict privacy standards, and significantly reduce token consumption. This approach safeguards sensitive information while retaining the benefits of advanced AI analytics.

Ultimately, the key is finding a balanced solution that preserves data confidentiality and operational efficiency, without sacrificing the powerful capabilities that LLMs bring to the table.