For modern quantitative (quant) trading firms, AI model and compute speed is the alpha. With hundreds of millions of dollars often riding on fractions of a percentage point in accuracy, developing high-performing AI models that run on reliable compute is essential. Traditionally, quant researchers focused on building the best-performing models and then handed them off to platform engineers to optimize for latency and cost in production. But at today’s scale of AI, that separation no longer works. Modern quant firms need to move faster, enabling teams to build the best models and optimize them on the fastest infrastructure simultaneously. Also adding to the complexity is the fact that existing model-building tools weren’t designed for the massive size of today’s models.