In this reflective progress report, the Fly.io team explains their ambitious but challenging journey with Fly GPU Machines—a product designed to let developers access Nvidia GPUs from within lightweight, containerized virtual machines. They recount the hurdles of integrating hardware GPUs with hypervisors like Intel’s Cloud Hypervisor, security concerns, and the surprising market behavior: while “GPUs aren’t going anywhere, but: GPUs aren’t going anywhere,” most software developers prefer to interface with established API services like OpenAI’s GPT or Anthropic’s Claude rather than managing GPUs directly. The report emphasizes that although significant investments were made, the core takeaway is the importance of learning from these bets as part of the startup journey.

Key points:

• Product Concept: Fly GPU Machines were created to help developers add AI/ML inference capabilities to their apps by providing fast CUDA-enabled virtual machines.
• Technical Hurdles: The integration with Nvidia’s ecosystem using Intel’s Cloud Hypervisor required extensive efforts, including “hex-edited” driver modifications and dedicated hardware, to meet the high standards of security.
• Security Concerns: GPUs present unique risks, prompting Fly.io to invest in costly security assessments by firms like Atredis and Tetrel and to use dedicated server hardware for GPU workloads.
• Market Realities: Despite the technical successes, the anticipated demand from developers didn’t materialize; most prefer using API calls to services like GPT, Claude, Replicate, and RunPod rather than handling direct GPU management.
• Startup Lessons: The company discovered that making bold bets—even when they don’t fully pay off—provides valuable learning experiences. They also highlight the importance of acquiring durable, tradable assets to cushion future risks.
• Forward Path: While Fly GPU Machines will continue to exist, no major enhancements are planned in the near term. The team’s focus remains on delivering an exceptional Fly Machine developer experience by prioritizing features that benefit the broader developer community.
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