DGX Spark
Local AI development, model experimentation, workstation-class workflows and responsible local inference habits.
From desktop to rack-scale
The goal is practical literacy: what systems can do, where their limits are, how data moves, how security is maintained, and when specialized infrastructure is needed.
Local AI development, model experimentation, workstation-class workflows and responsible local inference habits.
Professional workstation environments, GPU planning, storage, memory and deployment readiness.
High-performance AI acceleration, memory-aware workload planning and enterprise integration considerations.
Rack/pod thinking, networking, secure operations, monitoring, cost discipline and sovereign infrastructure planning.
Operator responsibility
Participants learn to connect workflow design with compute reality: throughput, memory, latency, privacy, governance, energy, cost and human oversight.