On August 26, the 2026 AGIC Shenzhen International General Artificial Intelligence Industry Expo officially opened its doors. Across 82,000 square meters of exhibition space, more than one thousand companies gathered, while over 7,000 overseas buyers engaged in on-site business negotiations. Large language models, computing infrastructure, robotics, and intelligent agent applications filled the halls, with numerous cloud vendors spotlighting end-to-end heterogeneous intelligent computing solutions purpose-built for agent workloads.
The phrase repeated most often throughout the expo was the shift in compute demand triggered by intelligent agents. Unlike the relatively centralized compute usage patterns of large-model training, agent operations encompass mixed workloads—thinking and reasoning, tool invocation, and multi-instance concurrency. Compute requirements fluctuate dramatically along both temporal and architectural dimensions, causing the traditional "just throw more GPUs at it" approach to buckle under the strain.
As a result, industry players universally advocate that agent mixed-load scenarios must not depend on any single accelerator type. Instead, GPUs, NPUs, and specialized AI accelerators need to be orchestrated through unified scheduling. By pooling heterogeneous compute resources with different performance and latency profiles and elastically dispatching tasks on demand, enterprises can preserve agent experience quality while compressing compute costs. This is the core logic driving heterogeneous intelligent computing from theory into practice.
Yet making heterogeneous intelligent computing work on the ground is no small undertaking. Once multiple chip families are deployed side by side, scheduling software must perceive each accelerator's capability profile and allocate tasks dynamically. It must also navigate a maze of complex states: high concurrency during an agent's thinking phase, low latency during tool invocation, and cost-efficient standby during nighttime idle windows. A single misstep in scheduling can cascade into wasted resources or degraded user experience.
This is precisely where StarWar Intelligent Computing Cloud has focused its efforts over the years: unified orchestration of heterogeneous resources. By bringing GPUs, NPUs, and other diverse compute units under one management platform and flexibly reusing them according to agent mixed-load characteristics, its architecture dovetails naturally with DiWorker's compute volatility management in multi-agent concurrency scenarios—sparing enterprises from having to provision dedicated resource stacks for every AI employee.
Zooming out to the broader industry landscape, the proliferation of heterogeneous intelligent computing signals a fundamental transition in compute supply: from "single-chip dependence" to "multi-chip collaboration." The scheduling software and metering-billing capabilities of computing platforms are becoming increasingly decisive competitive factors. For Shenzhen and the Greater Bay Area, a complete supply chain and dense compute infrastructure provide fertile ground for this trajectory to take hold.
Across the AGIC expo halls, "AI that can actually get work done" is everywhere—and the compute foundations supporting it are evolving in lockstep. Whether heterogeneous intelligent computing can genuinely bring down the cost of enterprise agent projects will determine how quickly agent deployment accelerates at scale.