Operator Fusion

Operator fusion is a technique where several small computation steps that would normally run separately are merged and processed together in one go.

Think of a kitchen. If you open the fridge, take out an ingredient, use it, and put it back, over and over for every step of cooking, all that opening and closing of the fridge door adds up to wasted time. Computers work the same way: after finishing one operation, they store the result in memory and then have to fetch it back out for the next operation, and this back-and-forth itself eats up time. Operator fusion is like prepping several cooking steps on the cutting board without opening the fridge each time — it bundles multiple computation steps together to cut down on how often data has to travel to and from memory.

This optimization matters especially when an AI model is actually running and producing answers. The bigger the model and the more computation steps involved, the more time gets spent shuttling data in and out of memory, which slows things down. By reducing this bottleneck through operator fusion, the same hardware can produce results faster.

In the article, Tencent's Hy4 preview model is described as analyzing the bottlenecks in its own inference system and repeatedly applying operator fusion and communication optimization, resulting in a 31.8% increase in throughput compared to the baseline. Here, "fusion" doesn't mean combining multiple models into one — it means merging computation steps to boost speed, so it shouldn't be confused with model merging.