At the recent NVIDIA GTC 2026 conference, the focus was on the evolution of AI from the training phase to the production inference phase. This shift signifies a significant development in the AI industry, moving beyond just developing faster chips and more intelligent models to addressing the challenges of running AI at scale in real-world applications.
Inference, the stage where AI models are put into production to deliver real products and customer experiences, is now at the forefront of the AI conversation. Factors such as cost efficiency, latency, orchestration, and uptime are becoming just as important as the accuracy of the models themselves.
The industry is now looking beyond just hardware advancements to the broader infrastructure needed to support AI-native companies. As AI becomes an integral part of business operations, the focus is on creating a cohesive system that encompasses chips, platforms, models, and applications to meet the demands of customers.
DigitalOcean, in collaboration with NVIDIA, announced the launch of the Agentic Inference Cloud, aimed at helping AI developers transition from experimentation to production seamlessly. This initiative includes the introduction of a new data center in Richmond designed specifically for AI inference, equipped with NVIDIA HGX B300 systems and a high-speed RDMA fabric. Additionally, DigitalOcean is integrating NVIDIA Dynamo 1.0 into its Kubernetes platform and expanding model options optimized for various use cases.
The momentum towards production-level AI deployment is already evident, with over 43,000 OpenClaw deployments on DigitalOcean, showcasing strong adoption from teams developing always-on assistants and agentic applications.
To further explore the practical aspects of running AI inference at scale, leaders from NVIDIA, VAST Data, vLLM, Arcee AI, Character.AI, Workato, and more will be sharing insights at the upcoming DigitalOcean Deploy event in San Francisco on April 28, 2026. This event aims to provide valuable lessons on real-world architecture, performance, economics, and operational efficiency in the field of AI inference.
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