Operationalization challenges
Deploying LLMs in enterprise settings involves complex AI and data management considerations and the operationalization of intricate infrastructures, especially those that use GPUs. Efficiently provisioning GPU resources and monitoring their usage present ongoing challenges for enterprise devops teams. This complex landscape requires constant vigilance and adaptation as the technologies and best practices evolve rapidly.
To stay ahead, it is crucial for devops teams within enterprise software companies to continuously evaluate the latest developments in managing GPU resources. While this field is far from mature, acknowledging the associated risks and constructing a well-informed deployment strategy is essential. Furthermore, enterprises should also consider alternatives to GPU-only solutions. Exploring other computational resources or hybrid architectures can simplify the operational aspects of production environments and mitigate potential bottlenecks caused by limited GPU availability. This strategic diversification ensures smoother deployment and more robust performance of LLMs across different enterprise applications.
Cost efficiency
Successfully deploying AI-driven applications, such as those using large language models in production, ultimately hinges on the return on investment. As a technology advocate, it is imperative to demonstrate how LLMs can positively affect both the top line and bottom line of your business. One critical factor that often goes underappreciated in this calculation is the total cost of ownership, which encompasses various elements, including the costs of model training, application development, computational expenses during training and inference phases, ongoing management costs, and the expertise required to manage the AI application life cycle.
