Agentic Applications Increase Inference Demand

This shifts the bottleneck from model quality to token delivery speed. A chatbot usually answers with one model pass, but an agent may plan, call tools, inspect results, switch models, and keep generating until the task is finished, so one user action can expand into many low latency decode loops. That is why SambaNova is positioning RDUs as the token generation layer inside agent stacks, rather than as a general replacement for the whole AI cluster.1 2 3 4 5 6

• SambaNova built its product around this pattern. Its cloud, dedicated systems, and managed deployments all run the same RDU stack, and the hardware is designed to keep many models resident and switch between them in microseconds, which matters when an agent hops between reasoning, coding, and voice models inside one workflow.1 2
• The broader market is converging on the same demand signal. Groq added Compound for multi step workflows with built in tools, and its own docs note that even one tool call turns one exchange into at least two inference calls. OpenAI framed Codex as a cloud coding agent that works on tasks in parallel, which is exactly the kind of usage that inflates token demand per user.3 4 7 8
• Competitors are solving this with different system designs. Cerebras is selling ultra low latency inference into coding and interactive workloads, while SambaNova and Intel are explicitly splitting jobs across GPUs for prompt processing, RDUs for decode, and Xeon CPUs for agent actions. That lets buyers plug specialized inference into existing data centers instead of rebuilding around one vendor.5 6 9

As software moves from chat windows to agents that do real work, inference demand will grow faster than user counts because each task fans out into more model steps. The winners in this market will be the vendors that can serve those steps quickly, cheaply, and inside mixed hardware environments, which is the lane SambaNova is now targeting most directly.1 3 5 6 9