Serial vs Parallel: The Engineering Trade-offs Behind Inference-Time Compute Scaling
When OpenAI’s o1 model spent unprecedented computational resources during inference, the AI community witnessed a paradigm shift: models could now trade thinking time for intelligence. But the real engineering challenge isn’t whether to scale inference compute—it’s how to scale it optimally. The choice between serial thinking (longer chains) and parallel thinking (more branches) fundamentally changes the cost-performance curve, and getting it wrong can mean burning 4x more compute for identical results. ...