Is hosting an open-source inference engine profitable? (userecurrence.ai)

A recent analysis has revealed that hosting an open-source inference engine may not be as lucrative as many believe, with profits heavily dependent on the specific GPU used and its utilization rates. The research shows that while the B300 GPU can be profitable once utilized above 35%, the H200 GPU fails to break even at any usage level, primarily due to its significantly lower throughput for FP4 weights. This discrepancy underscores the importance of efficient hardware in the inference market, where margins are razor-thin and competition is intense. The findings are critical for AI/ML startups and service providers, as they highlight the economic challenges in GPU rental and inference services. With rents rising much faster than anticipated and often surpassing the market value of the output, businesses must carefully manage both their hardware selection and pricing strategies to remain viable. The study emphasizes that relying solely on shared access via platforms like OpenRouter is insufficient; providers need direct customers to achieve sustainable operations. Understanding the intricacies of GPU performance, rental costs, and traffic patterns is key for those looking to navigate this competitive landscape successfully.