We were wrong about GPUs (fly.io)

Article summary

Fly.io, a cloud platform, invested in GPU-enabled machines for AI/ML workloads but found that the market demand was not as expected. The company spent significant time and resources on the project, including security assessments and custom hypervisor development, but ultimately decided to scale back its GPU ambitions. The main issue was that developers preferred using APIs for AI/ML tasks rather than managing GPUs directly. Fly.io learned that its initial assumption about the market was incorrect and is now focusing on other areas.

Main themes

• GPU adoption
• Cloud computing
• AI/ML workloads
• Market demand
• Hypervisor development
• Security concerns

What commenters say

• The article's admission of being wrong about GPU demand is a refreshing example of a company being transparent about its mistakes.
• The high cost and complexity of using GPUs in the cloud make them less appealing to developers, who prefer simpler API-based solutions.
• Nvidia's licensing and support for virtualized GPUs are seen as a major obstacle to adoption, with some commenters suggesting alternative approaches.
• Some commenters disagree with the article's conclusion, citing their own experiences with successful GPU-based projects and the potential for future growth.
• The article's focus on the challenges of using GPUs in the cloud overlooks the potential benefits of other approaches, such as using alternative hardware or cloud providers.
• The discussion highlights the importance of understanding market demand and being willing to pivot when initial assumptions prove incorrect.
• The use of GPUs in the cloud is not a one-size-fits-all solution, and different use cases may require different approaches to hardware and software configuration.