OpenAI has announced a multi-year partnership with Cerebras Systems to deploy 750 megawatts of ultra low-latency AI inference compute across OpenAI’s platform, significantly expanding its ability to deliver faster, real-time AI responses at scale.

According to both companies, the deployment will roll out in multiple phases beginning in 2026, with additional capacity coming online through 2028. Once fully deployed, the infrastructure is expected to represent the largest high-speed AI inference deployment announced to date.

The partnership centers on integrating Cerebras’ wafer-scale AI systems into OpenAI’s inference stack, adding a dedicated compute layer optimized for low-latency workloads such as real-time reasoning, coding, image generation, and AI agents.

OpenAI says the integration aligns with its broader compute strategy of matching specialized hardware to specific AI workloads. While large models increasingly support interactive and agent-based use cases, inference speed has become a limiting factor for real-time applications.

According to OpenAI, when AI systems respond more quickly, users engage more deeply, remain active longer, and are more likely to run higher-value workloads. Cerebras’ architecture is designed to reduce inference bottlenecks by consolidating compute, memory, and bandwidth onto a single wafer-scale chip, rather than distributing workloads across conventional GPU clusters.

“We will integrate this low-latency capacity into our inference stack in phases, expanding across workloads,” OpenAI said in its announcement.

Sachin Katti, Senior Vice President and General Manager of Networking and Infrastructure at OpenAI, said the partnership strengthens the platform’s ability to scale real-time AI interactions.

“OpenAI’s compute strategy is to build a resilient portfolio that matches the right systems to the right workloads. Cerebras adds a dedicated low-latency inference solution to our platform. That means faster responses, more natural interactions, and a stronger foundation to scale real-time AI to many more people.”

Cerebras positions the partnership as a milestone in bringing high-speed AI inference into mainstream use. The company’s wafer-scale processors are purpose-built to accelerate long outputs and real-time responses, particularly for large language models and AI agents.

According to Cerebras, models running on its systems can deliver responses up to 15× faster than GPU-based architectures, depending on workload. The company argues that inference speed is becoming a primary driver of AI adoption, similar to how broadband access transformed internet usage.

Andrew Feldman, co-founder and CEO of Cerebras, framed the partnership as part of a broader transition toward real-time AI.

“Just as broadband transformed the internet, real-time inference will transform AI, enabling entirely new ways to build and interact with AI models.”

Cerebras says the partnership builds on nearly a decade of informal collaboration between the two organizations. OpenAI and Cerebras were founded around the same time with distinct but complementary ambitions: OpenAI focused on developing advanced AI software and models, while Cerebras challenged conventional chip design by building a wafer-scale AI processor designed to overcome the limitations of traditional GPU-based architectures. According to Cerebras, teams from both companies have met regularly since 2017, sharing research, early technical work, and a common belief that continued growth in model scale would eventually require new approaches to hardware architecture. Cerebras says that convergence between large-scale models and specialized inference hardware is now taking shape through this partnership.

The companies say the 750MW deployment will come online in multiple tranches starting in 2026, with expansion continuing through 2028. The new capacity will be used to serve OpenAI customers, though neither company disclosed which specific products or tiers will access the Cerebras-backed inference layer first.

Once fully operational, Cerebras says its wafer-scale systems will support AI workloads for hundreds of millions of users, with the potential to reach billions as adoption expands.

Q: What is OpenAI announcing with Cerebras?
A: OpenAI is entering a multi-year partnership with Cerebras Systems to deploy 750MW of specialized AI compute designed to deliver ultra low-latency inference for OpenAI’s platform.

Q: What does “low-latency AI inference” mean in practice?
A: It refers to how quickly an AI system can generate a response after a user makes a request. Lower latency means faster replies, smoother conversations, and better support for real-time tasks like coding assistants, agents, and interactive applications.

Q: Why is OpenAI adding Cerebras to its compute stack?
A: OpenAI says its compute strategy focuses on matching the right hardware to the right workloads. Cerebras provides a dedicated inference solution optimized for speed, complementing other compute resources already used by OpenAI.

Q: When will this capacity be available?
A: Deployment will begin in phases starting in 2026, with additional capacity coming online through 2028.

Q: Who benefits from this partnership?
A: OpenAI customers will benefit from faster AI responses and improved real-time interactions, while Cerebras gains large-scale deployment of its wafer-scale AI systems.

As AI adoption moves beyond demonstrations toward everyday use, latency is becoming a critical constraint. Faster inference doesn’t just improve responsiveness — it enables new classes of applications, from conversational agents and coding assistants to real-time multimodal systems.

For OpenAI, the Cerebras partnership represents a strategic bet that specialized inference hardware will be essential to scaling real-time AI experiences. For Cerebras, it provides a path to deploy wafer-scale computing at unprecedented scale.

Whether this model reshapes how AI platforms architect their infrastructure will depend on performance, reliability, and cost as deployments expand. But the partnership signals a broader industry shift toward hardware–software co-design as AI systems move from batch processing to continuous, interactive use.

Editor’s Note: This article was created by Alicia Shapiro, CMO of AiNews.com, with writing, image, and idea-generation support from ChatGPT, an AI assistant. However, the final perspective and editorial choices are solely Alicia Shapiro’s. Special thanks to ChatGPT for assistance with research and editorial support in crafting this article.