In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) have emerged as powerful tools that transform how we interact with technology. However, training and maintaining these intricate systems come with a distinct set of challenges that demand robust solutions. 

Processing vast amounts of data is expensive

One of the foremost hurdles is the sheer computational power required. Training an LLM typically involves vast amounts of data processed through sophisticated algorithms, necessitating advanced hardware and extensive energy resources. 

According to an interview with the team at Bloomberg, building their AI model cost them over a million dollars and will likely take a similar amount to test and maintain it (here’s a full list of hardware costs that may be incurred).

Ensuring accuracy and ethics while eliminating bias is time-consuming and complex

Data quality is another pivotal challenge. The effectiveness of an LLM hinges on the training data’s relevance and diversity. Biases and inaccuracies in the data can lead to skewed outputs, undermining the model’s reliability. Therefore, rigorous data curation and ethical oversight are essential to harness the full potential of these models while ensuring fairness.

Model maintenance and monitoring are ongoing expenses

Moreover, maintaining an LLM is not a one-time effort; it requires continuous monitoring and updating to adapt to new information and evolving user needs. Implementing effective strategies for ongoing training and fine-tuning is crucial to keep the model relevant and operational. By acknowledging and addressing these challenges head-on, we position ourselves to optimize the capabilities of LLMs, ensuring they remain valuable assets in an increasingly digital world.

The costs associated with training and maintaining a large language model (LLM) can be substantial and multifaceted. Firstly, the financial investment in high-performance hardware, including powerful GPUs and extensive data storage solutions, is significant, often running into millions of dollars. Additionally, the energy consumption required during the training phase contributes to rising operational expenses, prompting organizations to consider energy-efficient alternatives.

Beyond infrastructure, there are costs linked to data acquisition, cleaning, and ethical oversight to mitigate biases and inaccuracies, which necessitate skilled personnel and time. Furthermore, ongoing maintenance demands continuous funding for updates and fine-tuning, ensuring the model adapts to changing contexts and remains effective. By understanding these costs, organizations can better allocate resources and develop strategic plans that maximize the return on their investment in LLMs.

Typically, the training process can take several weeks to months, depending on the model’s size and the complexity of the data being used. This extended timeframe is a direct consequence of the vast dataset that must be meticulously processed through numerous training iterations, with each epoch demanding substantial computational resources. And that introduces another challenge. In the time it takes to train the model, the dataset can quickly become stale and impact the quality of the results.

There are environmental considerations for some companies

In terms of energy expenditure, the consumption during training phases is remarkable. High-performance hardware, particularly GPUs, consumes a significant amount of electricity, contributing to increased operational costs and environmental considerations. For instance, some studies indicate that training state-of-the-art LLMs can result in carbon footprints comparable to that of an average car over its lifetime. 

Consequently, organizations must not only evaluate the economic implications of such energy use but also explore sustainable practices to mitigate their environmental impact. By strategically optimizing the training process — through techniques such as mixed precision training, distributed computing, and efficient algorithm design — organizations can significantly reduce the time and energy demands associated with LLM training, ultimately enhancing their operational efficiency and sustainability.

Rigorous testing for safety, security, and privacy requires expertise

Rigorous testing is paramount when validating the performance and robustness of a large language model (LLM). This process involves a comprehensive evaluation across various metrics, including accuracy, relevance, and bias detection. Initially, LLMs must undergo extensive benchmarking against established datasets to determine how well they generate responses and manage context. Additionally, scenario-based testing is crucial, where the model is exposed to real-world applications to assess its ability to handle nuanced queries and maintain contextual integrity. 

Moreover, determining the model’s resilience against adversarial inputs or unexpected user behavior is vital to ensure reliability and safety. Implementing a feedback loop that incorporates user interactions further refines the model, allowing for continual improvements. By prioritizing rigorous testing, organizations can confidently deploy LLMs, ensuring they meet the high standards required for effective and ethical use in diverse applications.

AI talent is expensive and in high demand

Building an AI engineering team involves several key cost components that can significantly impact the overall budget. Salaries are typically the most substantial expense, with qualified AI engineers, data scientists, and machine learning specialists often commanding high wages due to the demand for their expertise. According to the Wall Street Journal AI talent can command salaries up to $1 million dollars per year and tech companies have made a habit of poaching entire AI teams which would disrupt business continuity for many firms.

Summary

Building and maintaining a large language model (LLM) requires a substantial and strategic budget, which can vary significantly based on the model’s complexity and the organization’s specific goals. On average, organizations can expect to allocate between $2 million and $10 million for the initial development phase. This figure encompasses costs associated with high-performance hardware, data acquisition, and skilled personnel for data handling and ethical oversight.

Regarding ongoing maintenance, annual budgets can range from $500,000 to $2 million, accounting for updates, fine-tuning, infrastructure upkeep, and energy expenses. These costs ensure the model remains effective and responsive to emerging data and user needs. By understanding and planning for these financial commitments, organizations can secure the necessary resources to support the successful deployment and sustainability of LLMs, ultimately maximizing the value they deliver.

While there is no doubt about the power of AI to transform businesses through increased productivity and insights, creating an LLM is expensive, time-consuming, and requires a high level of expertise. Leveraging a vendor to take the burden off of you and ensure you reap the rewards of AI is an effective strategy and a way to ensure that your model is secure, accurate, and current.  Companies like Language I/O not only have the expertise to train models but also to ensure that they deliver the types of results that your customers expect from you and your brand.

Checklist

1. Development and Operation

• Engineering Salaries: Budget for salaries of data scientists, machine learning engineers, and software developers.
• Debugging: Allocate time and resources for debugging issues that arise during development.
• IDEs and Version Control Systems: Costs associated with integrated development environments (IDEs) and version control systems.
• Tooling to Monitor Model Performance: Invest in tools to monitor and manage model performance and health.
• Infrastructure Set-Up: Consider the costs of setting up and maintaining infrastructure.

2. Optimized ML Libraries/APIs

• Library and API Costs: Use more optimized machine learning libraries and APIs to decrease overall costs.

3. Licensing and Legal Considerations

• Code Licensing: Ensure all code used is properly licensed.
• Legal Compliance: Budget for legal consultations to ensure compliance with all relevant regulations.

4. Data Privacy and Security

• Regulatory Compliance: Adhere to data privacy laws and industry standards.
• Security Measures: Implement robust security measures to protect data and intellectual property.

5. Model Bias and Fairness

• Bias and Fairness Assessments: Conduct assessments to ensure the model is fair and unbiased.
• Ethical Reviews: Regular ethical reviews to identify and mitigate any potential issues.

6. Security Measures

• Adversarial Training: Incorporate adversarial training to protect against attacks.
• Other Security Measures: Implement additional security protocols to safeguard the model.

7. Deployment

• Production Environment: Plan for the deployment and maintenance of the model in a production environment.

8. Hardware and Cloud Costs

• GPU Costs: Consider the costs associated with GPUs, including memory requirements.
• Cloud Provider Costs: Compare costs across different cloud providers (AWS, GCP, Lambda Labs) and choose between spot and reserved instances.

Supporting Information

For a detailed explanation of each task and more insights into the costs, visit Brev’s blog post on LLM cost estimation.