Fill this form to download a August 2024 study on choosing the right storage solution based on the requirements and constraints of your deep learning scenario.

Cost-effectiveness can be optimized across many stages of the ML cycle. The current standard understanding, of course, is that the most resource-intensive part of the pipeline is the processing of the machine learning model on a GPU. If you can find a way to save in this area, half the battle is won.

However, the rapid development in our field has not only made models much more complex to process but has also increased their size. As a result, the decision on which storage to choose has become a critical factor affecting the overall cost-effectiveness of the project.

Based on Good-Better-Best framework, this practical study will help you navigate the available storage options on the market and maximize savings without compromising overall workload effectiveness and model performance.

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Igor Ofitserov
Technical Product Manager at Nebius