If you want to see how enterprise AI can deliver strong results without massive hardware sprawl, this video offers a practical and timely walkthrough. In this workshop, SambaNova’s Varun Krishna explains how the company’s full-stack AI platform helps teams run advanced agentic workflows with less infrastructure, lower power use, and faster inference. He starts by introducing SambaNova’s architecture, including its custom-built RDU chips, flexible deployment options, and focus on efficient AI inference for real business use cases.
A major theme of the session is energy-efficient performance. Varun explains why many enterprises are under pressure to control electricity costs and hardware demands as AI adoption grows. He shows how SambaNova’s RDU-based platform is built specifically for generative AI inference, giving organizations a way to run powerful models with better power efficiency than traditional GPU-heavy setups. For teams thinking about cost, sustainability, and scale at the same time, this part of the video makes a strong case for a different approach to enterprise AI infrastructure.
The workshop also gives viewers a clear look at model bundling, one of the most interesting capabilities covered in the session. Rather than running only one model at a time on dedicated hardware, SambaNova can host multiple models together on a single node and switch between them quickly. The video explains how this improves utilization for agentic workloads, where one planner model may be active constantly while other task-specific models are only needed at certain moments. It is a useful example of how SambaNova helps enterprises do more with the hardware they already have, instead of overprovisioning for every possible task.
What makes the session especially compelling is the live demo. Varun walks through a healthcare-focused agentic workflow built with LangGraph, Neo4j, and SambaNova-hosted models, showing how multiple models can work together in a single system. The demo covers validation, planning, tool selection, text-to-Cypher generation, graph-based retrieval, and response synthesis, all while comparing performance against an alternative provider. The result is a concrete example of how SambaNova supports fast, practical AI applications that combine orchestration, retrieval, and reasoning in a way enterprise teams can understand and build on.
The video closes with a hands-on component that makes it especially valuable for developers and technical teams. Viewers are shown how to get started with SambaNova Cloud, generate API keys, work with the provided GitHub repository, configure Neo4j, and run the project themselves. That blend of strategy, product insight, and real implementation makes this more than a high-level overview. Watch the full session to learn how SambaNova’s tools, hardware, and developer workflow can help your team build efficient enterprise AI systems with less complexity, less hardware, and more speed.