Nvidia expected to dominate AI compute as agentic inference evolves

The AI hardware landscape is shifting from model training to agentic inference, where AI systems autonomously reason, plan, and execute multi-step tasks. Nvidia is poised to dominate this new phase with its Blackwell Ultra platform, promising significant performance gains and cost reductions for these workloads. Agentic AI differs from generative AI by maintaining context and reasoning through complex logic, requiring production-scale architectures with persistent context memory rather than traditional stateless interactions. This evolution drives a need for co-designed hardware, integrating chips, memory, and software. Nvidia's strategy is evident in partnerships like VAST Data and its adoption by enterprise cloud providers such as DigitalOcean. For cloud providers, generic GPU clusters are becoming insufficient, necessitating specialized inference infrastructure. While decentralized GPU networks offer alternatives for training and basic inference, agentic workloads' demand for tightly integrated, low-latency architectures makes them harder to distribute. The challenge in AI is moving from building large models to deploying smart agents at scale, a problem Nvidia's Blackwell Ultra is architected to address.

(Source：Crypto Briefing)