Large Language Models (LLMs) are certainly remarkable. However, the cost of implementing them can be a substantial obstacle for many businesses. Training a good LLM can cost anywhere from hundreds of thousands to millions of dollars. Nevertheless, there are methods to adopt LLMs in a cost-effective manner for numerous use cases. This article will explore the various alternatives available to businesses aiming to adopt LLMs while keeping costs under control. By considering these alternatives, businesses can enjoy the advantages of large language models without exceeding their budget.
The High Cost of LLMs
Large language models have recently received significant attention due to their impressive natural language processing abilities. However, these models come with a high price tag. Although ongoing AI research and development are expected to refine models and reduce adoption costs, the waiting period may not be ideal for businesses aiming to be early innovators and gain a competitive advantage. Let’s examine why LLMs are expensive;
Training Cost
The computational resources needed to train LLMs can be substantial, and running inferences on large models can be computationally costly. LLMs require specific hardware and software to operate effectively. Businesses may need to invest in costly infrastructure to manage the processing demands of large language models. Some experts estimate that the training cost of GPT3 may have ranged from $4 million to $10 million or even more. Even though OpenAI currently offers ChatGPT for free, it wouldn’t be surprising if the company changes its decision and makes the tool available only to paying customers. Using OpenAI’s API services can be quite expensive. For instance, if you want to create a simple chatbot application that handles 100K requests per month, it would cost you around $4000 monthly. Furthermore, renting a single GPU instance on cloud computing platforms like Amazon Web Services (AWS) or Google Cloud Platform (GCP) can range from $0.50 to $5 per hour, depending on the type of GPU and the region.
Data Cost
Training AI models requires vast amounts of data, and labeling that data can be time-consuming and costly. Additionally, acquiring the data can be expensive. For instance, a study by Figure Eight found that creating a high-quality dataset for machine learning can cost between $1 to $100 per task, depending on the task’s complexity and the level of expertise required. Another study by Stanford University found that labeling a single image dataset for deep learning can cost as much as $3.50 per image. The training dataset for OpenAI’s GPT-3 was 45 terabytes in size. These figures show that the cost of training AI models, including data acquisition and labeling, is a significant investment.
Expertise Cost
The cost of adopting LLMs does not stop with hardware and data. Businesses must also consider the cost of hiring experts with the necessary skills and knowledge to manage and work with large language models. Training, fine-tuning, and deploying large language models require highly specialized skills that may not exist in an organization’s workforce. Hiring such individuals can be costly and further increase the overall adoption cost. Moreover, the demand for individuals with expertise in AI and machine learning has significantly increased in recent years, leading to a shortage of such talent in the market. Consequently, the cost of hiring and retaining these individuals has substantially increased. According to a LinkedIn report, AI specialist roles are among the top emerging jobs, with an average base salary of $136,000 per year in the US. The same report also found that the demand for AI specialists has grown by 74% annually in the past four years.
Adopting LLMs Cost-Effectively
There are several strategies that businesses can employ to reduce costs and make LLM adoption more affordable.
- Fine-tune open-source pretrained LLMs
- Use training accelerators
- Optimize your LLMOps
- Select a lightweight LLM that meets your objective
- Employ a tech
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