Huawei introduces new full-stack AI Data Center infrastructure to accelerate enterprise adoption
Paris, May 25
Huawei unveiled a full-stack data infrastructure solution for artificial intelligence data centers to accelerate construction and large-scale AI adoption for enterprises.
Yuan Yuan, Vice President of Huawei and President of the Huawei Data Storage Product Line, announced the framework during a keynote speech at the Huawei Innovative Data Infrastructure (IDI) Forum 2026 held on May 21.
According to the company, AI agents are transforming enterprise operations and evolving into "digital employees."
To support this shift, the newly introduced architecture systematically targets several core operational pillars, which include data lakes, AI data platforms, compute power, models, agent frameworks, and data resilience.
As per Huawei, the infrastructure utilizes high-density OceanStor Pacific Scale-Out Storage to deliver 11 PB capacity in a 2 U space for massive data storage. This is paired with DME Omni-Dataverse, a unified data space solution that supports multimodal, cross-site, and real-time data import alongside global data visibility.
For ultra-scale inference clusters, the company introduced a Context Memory Storage (CMS) supporting heterogeneous computing power. This system expands into a PB-scale shared KV cache pool, which reduces the time to first token (TTFT) by 90 per cent.
For enterprise inference scenarios, the framework integrates an AI data platform combining KV cache acceleration, a knowledge base with over 95 per cent retrieval accuracy, and an evolving memory bank. Managed by a Unified Cache Manager (UCM), the integration improves inference accuracy by 30 per cent.
According to the company, a dedicated ModelEngine provides model gateway capabilities for zero-code adaptation and one-click deployment. Through fine-grained compute resource partitioning, it achieves an up to 1:10 ratio of xPU partitioning, allowing one xPU to serve multiple purposes.
Development is further supported by the ModelEngine Nexent agent platform, which generates agents through natural language-based interaction, reducing rollout time by 80 per cent. The full-stack solution also incorporates a data resilience platform designed to prevent tool misuse, data poisoning, tampering, and ransomware attacks across agents, models, and infrastructure.
Yuan emphasized the critical role of data asset protection and industrial collaboration during the presentation.
"AI is unlocking new opportunities for the IT industry," Yuan said. "The next chapter of AI is data. Committed to technological innovation in data storage, Huawei will accumulate the experience of industrial AI adoption, and work closely with the entire industry to help customers accelerate their journey into the intelligent era."
— ANI
Reader Comments
90% reduction in time to first token? That's impressive. In India, we are seeing a surge in AI-based customer service chatbots. This could make real-time responses much smoother. But I'm a bit skeptical—will this work with legacy Indian IT systems? Many enterprises here still run on older infrastructure. 🧐
The concept of "digital employees" is interesting, but we need to be careful about job displacement in India's IT sector. This could be a double-edged sword—while it boosts efficiency, thousands of professionals might need to reskill. Hope Huawei's solution also includes training support for local talent.
As an AI engineer working in Bangalore, I'm excited about the ModelEngine with zero-code adaptation. Indian SMEs often lack deep AI expertise, so this could democratize AI adoption here. However, I worry about data sovereignty—will Huawei comply with India's upcoming data protection laws? That's a big question.
The data resilience platform is crucial. With ransomware attacks on the rise in India—remember the recent attacks on banks and hospitals?—having protection across agents, models, and infrastructure is not just a feature, it's a necessity. 👏
A unified data space like DME Omni-Dataverse sounds great in theory, but integrating it with the diverse data formats used in Indian enterprises (from ERP systems to legacy databases) will be a challenge. Either way, this is a step forward for AI infrastructure globally. Let's see how it performs in real-world Indian scenarios.
We welcome thoughtful discussions from our readers. Please keep comments respectful and on-topic.