Large language models have gained significant attention in recent years due to their impressive capabilities in generating human-like text. However, these models come with a hefty price tag, especially when considering enterprise usage. In this article, we will explore the true cost of large language models and the factors that influence their expense.
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Use Case: Determining the True Cost
When it comes to deploying generative AI for enterprise purposes, it’s crucial to understand the specific use case. The cost factors differ significantly depending on the desired application. For example, a consumer chatbot may cost as little as $25 per month, but utilizing generative AI for enterprise-level tasks involving sensitive data comes with additional considerations and costs.
To ensure the best fit for your enterprise, it is recommended to work with a platform partner or vendor that allows you to participate in a pilot. This way, you can identify your pain points and evaluate if generative AI is the right solution for your enterprise. By experimenting with different models and tuning methods, you can customize the technology to suit your specific requirements and optimize cost efficiency.
Model Size: Impact on Pricing
The size and complexity of a generative AI model directly impact pricing. Vendors offer different pricing tiers based on the model’s size in terms of parameters. For example, models like FLAN (11 billion parameters), Granite (13 billion parameters), and Llama 2 (70 billion parameters) serve different use cases ranging from language translation to Q&A.
It is essential to assess whether a vendor allows you to access various models or if they lock you into a single model for all use cases. Additionally, evaluating a vendor’s focus on continuous innovation regarding proprietary models can provide advantages in domain-specific task generation.
Pre-Training: From Scratch or Pre-Trained?
Pre-training a language model from scratch can be a costly endeavor due to the extensive compute time and effort required. This approach gives enterprises control over the training data but comes with a significant price tag. For example, the cost of pre-training GPT-3 involved over 30,000 GPUs over a 30-day period, resulting in a total cost of over $4.6 million.
Alternatively, leveraging a pre-trained model can be a more cost-effective option. Many vendors provide pre-trained models that enterprises can utilize without the need for extensive pre-training.
Inferencing: Generating Responses
Inferencing refers to the process of generating responses using a language model. The cost of inferencing is calculated based on the number of tokens processed, which include both the prompt and the completion. Tokens are discrete units of information, with approximately 100 tokens equating to 75 words.
To achieve tailored results without extensive model alteration, enterprises can utilize prompt engineering. This involves crafting effective prompts to elicit desired responses from the language model, without altering the model’s parameters. Prompt engineering is a cost-effective way to achieve customization.
Tuning: Customizing Model Performance
Tuning involves adjusting the internal settings or parameters of the model to optimize performance. Fine-tuning extensively adapts the model by modifying parameters, resulting in a separate forked version that requires hosting. This approach is suitable for specialized tasks where performance is critical and requires a large amount of labeled data for optimization.
Parameter-efficient fine-tuning aims to achieve task-specific performance without extensive model changes. It allows tuning smaller models without altering the underlying structure. Evaluating the cost of labeled data acquisition is crucial when considering tuning options.
Working with a partner or vendor that offers both fine-tuning and parameter-efficient fine-tuning methods allows you to select the most cost-effective solution for your enterprise’s needs.
Hosting: API Inference vs. Forked Models
Hosting a model becomes necessary when fine-tuning or utilizing a custom model. API inference is cost-effective when using pre-trained models or employing prompt engineering techniques. The cost for API inference is based on the number of tokens processed.
On the other hand, hosting a model involves deploying and maintaining it for custom purposes. Hourly costs are incurred for hosting, depending on the time of interaction with the model. Companies that require constant access to their models benefit from hosting, but this comes with additional expenses.
Choosing a partner or vendor that offers various ways to interact with the model, whether through API inference or hosting, provides flexibility and cost optimization.
Deployment: Cloud vs. On-Premise
The choice between cloud and on-premise deployment depends on industry regulations and specific business needs. Cloud deployment through Software-as-a-Service (SaaS) offers a predictable subscription fee structure, eliminates the need for procuring hardware, and provides scalability.
On the other hand, on-premise deployment suits industries with regulations that require data to be kept in-house. Organizations opting for on-premise deployment must purchase and maintain GPUs while gaining full control over the infrastructure.
By selecting a partner or vendor that can cater to your deployment preferences, you can ensure a cost-effective and regulatory-compliant solution.
Conclusion
Large language models offer tremendous potential but come with significant costs for enterprise usage. Understanding the factors that influence pricing, such as the use case, model size, pre-training, inferencing, tuning, hosting, and deployment, is crucial in determining the true cost.
Working with a reliable partner or vendor like Techal, who offers expertise and a range of customizable options, allows enterprises to leverage the benefits of generative AI while optimizing costs. To learn more about Techal’s technology services, visit Techal.
FAQs
Q: How much does generative AI cost for enterprises?
A: The cost of generative AI for enterprises varies based on factors like the use case, model size, pre-training, inferencing, tuning, hosting, and deployment. It is recommended to work with a partner or vendor to evaluate your specific requirements and determine the most cost-effective solution.
Q: Can I use a pre-trained model for enterprise purposes?
A: Yes, pre-trained models provide a cost-effective option for enterprise usage without the need for extensive pre-training. Different models suit different use cases, so it’s essential to evaluate which model best fits your requirements.
Q: What is the difference between fine-tuning and parameter-efficient fine-tuning?
A: Fine-tuning extensively adapts the model by modifying parameters, resulting in a separate forked version. Parameter-efficient fine-tuning achieves task-specific performance without structural changes to the model, making it a more cost-effective option in certain scenarios.
Q: Should I choose cloud or on-premise deployment for generative AI?
A: The choice depends on industry regulations and specific business needs. Cloud deployment offers scalability and predictable subscription fees, while on-premise deployment provides full control over infrastructure and compliance with data regulations. Evaluate your requirements and consult with a partner or vendor to determine the best deployment option for your enterprise.
