The transition from training generative AI models to agentic AI inference represents a fundamental shift in compute requirements toward more and more agile infrastructure. While chatbots operate on linear, user-driven queries, agents function autonomously — planning, reasoning, and executing multi-step workflows. They chain together specific "expert" models for coding, math, or creative writing in real-time. This dynamic behavior creates a nightmare for traditional infrastructure: You no longer know which model needs to run next.
As Salesforce recently noted, “The rise of agentic AI presents a unique infrastructure challenge because its unpredictable, bursting workloads demand a shift from traditional reactive cloud scaling to an intelligent, predictive, and resilient foundation.”
In this new paradigm, static infrastructure fails. The ability for hardware to adapt instantaneously to these bursting workloads — switching between expert models without latency penalties — is no longer a luxury; it is the prerequisite for viable agentic AI. This brings us to the critical bottleneck of modern AI inference: the speed of hot swapping.
To address these challenges with AI Infrastructure, today we are launching configurable model bundles on SambaStack powered by Reconfigurable DataFlow Units (RDU), which deliver significantly faster switching times compared to traditional GPU architectures and inference frameworks like vLLM.
