BEIJING — In a major push to bolster domestic technological independence amidst tightening foreign trade controls, Chinese tech titan Baidu has revealed an ambitious roadmap for proprietary artificial intelligence hardware and advanced inference architectures.

During its flagship annual technology conference held in Beijing, Baidu executives shifted the industry spotlight toward operational efficiency. Company leadership emphasized that the future growth engine of the semiconductor market is transitioning away from massive training workloads and toward real-time inference, fueled heavily by the proliferation of autonomous AI agents.

Scaling Up with the "Tianchi 1024" System

The centerpiece of Baidu’s hardware announcements was the introduction of the "Tianchi 1024" supernode infrastructure, slated for commercial rollout in 2027. Designed to link 1,024 dedicated AI accelerators into a unified computational fabric, the upcoming system promises a threefold boost in both data transmission bandwidth and inference performance, alongside a 2.5-fold increase in training efficiency compared to preceding architectures.

Industry analysts note that interconnect latency remains a primary bottleneck when clustering thousands of independent chips. To counter this, Baidu’s new framework leverages advanced high-speed networking and optical communication protocols to maintain high throughput across its accelerator arrays.

Complementing the 2027 supernode timeline, Baidu outlined a steady cadence of silicon innovation. Following the deployment of its cost-optimized M100 chip earlier this year, the company plans to introduce the high-throughput M300 processor next year, followed by an entirely redesigned "N Architecture" product line scheduled for 2028.

Expanding Model Ecosystems and Cost Reduction

Beyond silicon, Baidu is actively shaping the software and platform layer. The company officially showcased "CiyuanSu," a curated B2B marketplace for AI models designed to streamline procurement while enforcing rigorous quality checks, performance verification, and supplier credit grading. Within a month of its debut, the platform reportedly surpassed 50 million daily model calls and 700 billion processed tokens.

Baidu also highlighted the latest iterations of its foundational technology, including the ERNIE 5.5 large language model and two specialized application engines: a Deep Thinking Model optimized for ultra-low latency web search inference, and a Micro-Expression Model aimed at hyper-realistic digital avatar and video synthesis. According to corporate figures, these specialized models have lowered cloud inference expenditures by over 30 percent and halved digital video production costs.

Redefining the Corporate Workforce

Addressing the broader societal impact, Baidu’s leadership framed the AI transition not just as an IT upgrade, but as a fundamental "organizational revolution." As generative workflows and AI agents dismantle traditional departmental silos, enterprise structures are expected to transition from static job descriptions to fluid, task-oriented ecosystems.

Baidu reported internal validation of this shift, noting that active daily internal AI agents have multiplied sixfold over the past year to 37,000 units, handling over one million weekly tasks. Furthermore, the company’s multi-step digital assistant tool, "Baidu Dazi," has seen its single-pass verification rate for complex workflows climb from 54.6% to 88.2%, signaling a maturing capability for autonomous business execution.

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