Large AI models and multi-agent systems create an enormous memory challenge. Performance is determined not simply by processing power, but by how quickly model data can be accessed and moved when it is needed.

In this presentation, SambaNova Chief Architect Sumti Jairath explains the company's memory-centric approach to solving this problem.

SambaNova combines a three-tier memory architecture with Reconfigurable Dataflow Units (RDUs), allowing large models to be held and accessed efficiently while computation is organised around the flow of data through the workload.

This architecture is particularly important for agentic AI. Where multiple agents or models are involved in a workflow, the infrastructure may need to move rapidly between them while maintaining low latency. SambaNova describes its architecture as enabling switching between hundreds of agents in microseconds.

The result is an approach designed to support trillion-parameter models, large-scale generative AI and increasingly complex multi-agent workflows without relying solely on conventional GPU architecture.