Training and deploying LLMs can be a costly activity. This is because training and deploying LLMs requires substantial computational power, including high-performance GPUs. These computing resources are used for running multiple iterations of complex algorithms on massive datasets when training, as well as for fine-tuning and for deployment. The cost of renting or owning these resources, along with the electricity required to run them, contributes to the overall expense.
Training a foundation model is so expensive–GPT-4 was trained on a cluster with 25k GPUs for over a month, at an estimated $10M–so it’s really not feasible for any but a select few technology companies. However, there is a large and growing landscape of open and commercial LLMs that organizations can leverage and customize.
Customizing an LLM–whether via prompt engineering or fine tuning and transfer learning–still comes with significant costs. MLRun and Nuclio are two open source solutions that can help optimize these customized LLM deployment costs.
MLRun is an open source MLOps orchestration framework. MLRun can help optimize training costs by using state of the art algorithms and automated technologies, like quantization and distributed training. Quantization enables using smaller GPUs, which are more cost-effective, and distributed training allows faster training or even enables the training itself if the user doesn't have access to large enough GPUs.
To optimize serving, MLrun leverages Nuclio. Nuclio automates the data science pipeline with serverless functions. By leveraging those serverless advantages, Nuclio auto-scales and employs on-demand resource utilization automatically at each step of the LLM serving pipeline.By only using the amount of computational resources required for performing training, serving and all other steps of the MLOps pipeline, organizations can optimize cost and make the most out of their already available resources (using quantization and distributed training).