The presentation discusses the challenges of training deep learning models on-premises with GPU infrastructure, highlighting the need for scalable and efficient solutions. The author proposes a framework called Lambda, which enables the creation of custom, on-prem GPU training infrastructures tailored to specific deep learning use cases. This approach allows developers to build and deploy their own GPU-accelerated environments, reducing reliance on cloud-based services and enabling more control over data privacy and security. By leveraging Lambda, organizations can optimize their deep learning workflows for better performance, scalability, and cost-effectiveness.