Aviz Networks Discusses DSX Air Simulation-First AI Factory Validation
Aviz Networks and NVIDIA discussed shifting AI infrastructure deployments from hardware-first setup to simulation-first design, using DSX Air to validate connectivity, configurations, and security before hardware arrives.
Research Overview
The webinar focused on how enterprises can deploy AI infrastructure faster and with fewer integration delays as AI factories become more complex.
Aviz Networks was represented by Ilona Gabinsky as moderator, with David Iles from NVIDIA and Vishal Shukla participating in the discussion.
Key Findings
The session described a move toward simulation-first design rather than waiting for hardware before testing configurations and integrations.
It also framed shift-left as an approach that moves validation earlier in the infrastructure lifecycle to reduce time to production.
Technical Breakdown
Simulation-first deployment was presented as a way to test connectivity, configurations, security policies, upgrade workflows, and failure scenarios before physical delivery.
Hardware performance benchmarking was identified as a case that requires raw performance evaluation on actual systems, while other validation steps can be addressed through simulation.
Product Update
Aviz Networks was described as providing orchestration, operations, and visibility across the AI factory stack.
According to the webinar summary, Aviz Networks supports network orchestration for Day 0 through Day 2, along with multi-tenancy, automation, security and observability, and AI-driven operations.
Operational Impact
Under the shift-left framing, teams can validate earlier and then deploy with fewer late-stage changes tied to integration problems.
The discussion compared the traditional sequence of hardware setup followed by testing with a simulated sequence of simulate, validate, then deploy.
The webinar’s main takeaway is that AI factories need tight integration across multiple layers, and simulation-first deployment with shift-left validation is positioned as a way to reduce deployment time and integration risk for enterprise teams managing AI infrastructure.