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Large Language Models(LLMs) are undoubtedly impressive. However, the adoption cost can be a significant hurdle for most businesses. Training a decent LLM can cost hundreds of thousands to millions of dollars. However, there are ways to embrace LLMs cost-effectively for many use cases. This article will discuss the various options available to businesses looking to adopt LLMs while controlling costs. By considering these options, businesses can reap the benefits of large language models without breaking their budget.
Large language models have gained significant attention recently due to their impressive natural language processing capabilities. However, these models come at a steep cost. Although ongoing research and development in AI are expected to refine models and bring down adoption costs, the waiting period may not be favorable for businesses seeking to be early innovators that can gain a competitive edge. Let’s see why LLMs are costly;
The computational resources required to train LLMs can be significant, and running inferences on large models can be computationally expensive. LLMs require specialized hardware and software to run effectively. Businesses may need to invest in expensive infrastructure to handle the processing requirements of large language models. Some experts estimate that the training cost of GPT3 may have cost anywhere from $4 million to $10 million or even more. Even though OpenAI offers ChatGPT as free for now, it won’t be surprising to see the company changing its decision and making the tool accessible only to paying customers. Using OpenAI’s API services can be quite costly. For example, if you want to build a simple chatbot application that handles 100K requests per month, it would cost you around $4000 monthly. Moreover, renting a single GPU instance on cloud computing platforms such as 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.
Training AI models require vast amounts of data, and labeling that data can be time-consuming and expensive. Additionally, it can be costly to acquire the data. For example, one study by Figure Eight found that the cost of creating a high-quality dataset for machine learning can range from $1 to $100 per task, depending on the complexity of the task and the level of expertise required. Another study by Stanford University found that the cost of labeling a single image dataset for deep learning can be as high as $3.50 per image. The training dataset for OpenAI’s GPT-3 was 45 terabytes in size. These numbers demonstrate that the cost of training AI models, including data acquisition and labeling, is a significant investment.
The cost of adoption of LLMs does not end with hardware and data. Businesses must also consider the cost of hiring subject matter experts with the skills and knowledge necessary to manage and work with large language models. Training, fine-tuning, and deploying large language models require highly specialized skills that may not be present in an organization’s workforce. Hiring such individuals can be expensive and further increase the overall cost of adoption. Moreover, the demand for individuals with expertise in AI and machine learning has risen significantly in recent years, leading to a shortage of such talent in the market. As a result, the cost of hiring and retaining these individuals has increased substantially. According to a report by LinkedIn, 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.
There are several strategies that businesses can use to reduce costs and make LLM adoption more affordable.
With careful planning and strategic thinking, you can reduce costs for LLM adoption. You must clearly understand the organization’s specific needs and goals. Based on that, you can select the most appropriate models and strategies that meet their objectives while minimizing costs.
One effective strategy is to use open-source pre-trained models rather than training a model from scratch. This approach can help businesses save time and resources while achieving their desired outcomes. Pre-trained models are trained on large amounts of data, allowing them to identify patterns and relationships. For example, a pretrained LLM can already process natural language. You only need to fine-tune the model to increase its accuracy for the task you need the model to perform. On the other hand, if you are training a model from scratch, you’ll need to bear with pre-training costs.
OpenAI released GPT-3 and GPT-4 as closed-source models. However, EleutherAI responded by launching an open-source alternative named GPT-Neo, which has demonstrated comparable results. Similarly, when DALL·E 2 was introduced, an open-source version was also made available by Stability AI under the name Stable Diffusion. Using open-source pre-trained models can help businesses start more quickly and with fewer resources. Such models have already undergone extensive training and fine-tuning, and they can often provide better results than models trained from scratch.
One example of a fine-tuned open-source model is LLaMa. It is a pre-trained language model that can be fine-tuned for specific natural language processing (NLP) tasks, such as sentiment analysis, question answering, and text classification. Many businesses have used LLaMa to develop NLP applications specific to their industry or use case, such as healthcare, finance, or legal.
Several open-source AI models are available in the market. But, relevant information about these models is scattered on the internet, and it is extremely difficult to evaluate these models. To solve this problem, we recently launched a leaderboard to help researchers easily identify the best open-source models with an intuitive leadership quadrant graph. We evaluate the performance of open-source models to rank them based on their capabilities and market adoption.
Training accelerators can be crucial in reducing costs when fine-tuning AI models. These accelerators are specialized hardware designed to expedite the training process of AI models, enabling faster and more efficient computation. By utilizing training accelerators, companies can save time, reduce energy consumption, and cut costs. Some examples of widely used training accelerators include GPUs, TPUs, and custom ASICs.
GPUs like NVIDIA’s Tesla V100 and A100 have been the go-to choice for training AI models. They are designed to handle parallel computations, a common requirement for AI training tasks. Compared to traditional CPUs, GPUs can speed up training time by 10-50 times, depending on the specific task and model. This reduction in training time directly translates to reduced energy consumption and associated costs.
Google introduced TPUs in response to the growing demand for AI training accelerators. TPUs are custom-designed for handling tensor computations, which are at the core of deep learning algorithms. Google says their TPU v3 is eight times more energy-efficient than the equivalent GPU (V100) for AI training tasks. This results in significant cost savings regarding energy consumption and infrastructure requirements.
Some companies have started to develop their custom Application-Specific Integrated Circuits (ASICs) to address the unique requirements of their AI workloads. For example, Graphcore’s IPU (Intelligence Processing Unit) claims to offer a 10-100 times speedup in training times compared to traditional GPUs. Custom ASICs can provide substantial cost savings by optimizing the hardware for the AI algorithms.
When comparing training accelerators, it’s important to consider the specific requirements of a given AI model and the business’s infrastructure. For instance, TPUs and custom ASICs offer greater cost savings in terms of energy efficiency and training times compared to GPUs. However, they may also require more specialized infrastructure and expertise, which can offset some of the potential cost savings.
LLMLOps, or Large Language Model Operations, is the set of practices and tools to manage the lifecycle of large language models like GPT-4. Architecting the right LLMLOps strategy can help businesses reduce costs by streamlining development, deployment, and maintenance processes when embracing AI models. The right LLMLOps strategy focuses on efficient resource utilization, optimizing model performance, and continuous improvement.
A proper LLMLOps can help businesses allocate resources more effectively, reducing idle time and unnecessary overhead. Businesses can optimize costs associated with running AI models by automatically scaling compute resources according to demand and using resource management tools. For example, Kubernetes can orchestrate containerized AI workloads, enabling dynamic resource allocation and reducing infrastructure costs.
The right LLMLOps helps in continuous model optimization, which involves fine-tuning model architectures, pruning, and quantization techniques. For instance, model pruning can reduce a model’s size by up to 90% without significant loss in performance, enabling faster inference and lower storage costs. Quantization, however, can compress model weights, allowing faster and more efficient computations with reduced memory and power requirements. These optimizations lead to reduced operational costs and improved performance.
LLMLOps also encourages adopting continuous integration and continuous deployment (CI/CD) practices, automating, integrating, testing, and deploying AI models. This process helps businesses minimize the time and effort required to roll out new features and improvements, reducing operational costs. Moreover, it also helps monitor AI models’ performance and proactively addresses potential issues. Businesses can identify and address issues early by implementing monitoring tools that track model accuracy, latency, and resource utilization, reducing downtime and associated costs.
A well-architected LLMLOps strategy emphasizes collaboration among teams and the reusability of components. By creating a shared repository of pre-trained models, code, and data, businesses can reduce the time and effort required to develop new AI models.
Lightweight models have fewer parameters and are generally smaller, resulting in faster training and inference times, lower memory and storage requirements, and reduced energy consumption. These benefits directly translate to cost savings for businesses.
While lightweight models are generally faster and more cost-effective, they may have lower accuracy than their larger counterparts. You should weigh the trade-off between model size and accuracy for your use case. For example, a customer support chatbot may require a lower level of accuracy, while a legal document analysis tool might prioritize accuracy over speed and cost. Moreover, lightweight models generally have faster inference times, making them suitable for cases where real-time responses are crucial, such as voice assistants or translation services.
DistilGPT is a lightweight version of the GPT model, created using the distillation process, which compresses the knowledge of a larger model into a smaller one. DistilGPT has fewer parameters than the full GPT model, leading to faster training and inference times while maintaining a high-performance level. DistilGPT can be used for tasks like text summarization, sentiment analysis, or chatbot applications where real-time responses are essential.
Hiring companies like Accubits, which offers Generative AI development services, can be better than setting up an in-house development team. The benefits are;
Faster access to expertise: By hiring a tech partner, businesses can benefit from their industry knowledge, best practices, and state-of-the-art techniques, ensuring the development of high-quality AI models. In contrast, building an in-house team may require extensive training and time to achieve the same level of expertise, which could delay the project’s progress and increase costs.
Cost Efficiency: Setting up an in-house team for LLM-based development can be expensive, especially considering the costs associated with recruiting, training, and retaining skilled personnel. Additionally, businesses must invest in the infrastructure and tools required for LLM development. Hiring a tech partner can be more cost-effective, as they already have the resources and infrastructure.
Faster Time to Market: Tech partners are often better equipped to deliver projects within tight deadlines with their specialized expertise and established processes. They can quickly ramp up or scale down their team as needed, ensuring that projects are completed on time.
Risk Mitigation: Developing LLMs can be a complex process with various risks, such as algorithmic bias, data privacy concerns, and regulatory compliance. A tech partner with experience in LLM development can help businesses navigate these risks more effectively.
Businesses seeking to adopt large language models can benefit from several cost-saving strategies. These approaches can help you reduce costs while achieving your desired outcomes and realizing the benefits of AI-powered solutions. If you plan to build a product using LLMs, we advise you to get started with an AI consultation. Our AI experts bring in-depth knowledge and experience, helping you identify the most suitable LLM for your specific use case, optimize model performance, and navigate potential challenges. By beginning with an AI consultation, businesses can develop a comprehensive understanding of their needs, set realistic goals, and establish a clear roadmap for LLM implementation. This proactive approach saves time and resources and reduces the risk of costly mistakes and rework.
