That results in dozens to hundreds of LLM calls chained together, each passing growing context to the next, with tool calls like code compile and execution, database search and web browsing at every handoff. The complexity isn’t additive; it’s multiplicative.

The distinction matters enormously for performance measurement. Existing AI inference benchmarks measure one LLM call: how fast an LLM responds to a single request and how many simultaneous requests a system can handle. They weren’t designed for agentic workloads, where chained LLM calls, tool call delays and growing context stress accelerated computing systems in fundamentally different ways than a single LLM call ever could.

For companies building and deploying agents at scale, it’s important to understand how responsive agents are, how many can be deployed simultaneously and how much useful work AI infrastructure can deliver for every dollar and watt invested.

In this first round, AgentPerf measures agentic performance with DeepSeek V4 Pro, a large mixture-of-experts (MoE) model that represents the class of frontier models powering today’s most capable agents. On this workload, NVIDIA GB300 NVL72 delivers the highest performance in the benchmark, running up to 20x more agents per megawatt than the NVIDIA HGX H200 system.