How Index Ventures Jumped to the Front of the AI GPU Line

This podcast episode discusses the impact of graphic processing units (GPUs) on AI technology development. It highlights the significant role of GPUs in running machine learning models, particularly transformer models with massive parameters. The discussion addresses the challenges faced by AI companies in acquiring GPUs due to limited availability and long lead times. To address this issue, Index has partnered with Oracle to provide GPUs to early-stage portfolio companies. The episode emphasizes the importance of removing barriers to access compute resources for AI startups to thrive and innovate. It also touches on the value of early team collaboration, the need for tailored model providers, and the challenges faced by investors in ensuring portfolio diversity. The chapter concludes by exploring the GPU program for companies performing fine-tuning and the potential of AI integration in underserved markets. Takeaways • GPUs have become essential for running machine learning models, especially transformer models with massive parameters. • Acquiring GPUs is challenging for early-stage AI startups due to limited availability and long lead times. • Index has partnered with Oracle to provide GPUs to their earliest stage portfolio companies, enabling startups to focus on their core development process. • Early team collaboration and connections with companies like Nvidia and Oracle are crucial for AI startups. • The emergence of companies that assist in managing workflows and information sharing in AI is helping navigate the shortage of computing resources. • Government programs, such as Access run by the National Science Foundation, provide computing power for AI startups. • AI startups need tailored model providers to meet the specific needs of different industries. • Investors face challenges in ensuring portfolio companies do not overlap, and diversity is important. • The GPU program caters to companies performing fine-tuning and struggling to access dedicated GPUs. • The application layer of AI has potential in specific underserved markets, while infrastructure improvements are needed for implementing large language models. • Model routing, based on cost, performance, and security, plays a critical role in enterprise applications.