Where Does the Energy Go? Profiling LLM Agent Inference on Blackwell GPUs

LLM agents that iteratively reason, plan, and invoke tools create workload profiles fundamentally different from single-pass inference, yet how their energy consumption is distributed across hardware components and workload phases remains poorly understood. Characterizing these workloads therefore requires simultaneous visibility into both component-level power and phase-level execution. We conduct a full-stack energy profiling study combining NVML GPU counters, Intel RAPL CPU/DRAM counters, and Intelligent Platform Management Interface (IPMI) system-level sensors on 2$\times$ NVIDIA RTX PRO 6000 Blackwell GPUs, and profile three representative workloads with Qwen3.8-27B. For mathematical reasoning, we compare thinking-enabled and thinking-disabled modes. Our measurements reveal that GPU-only telemetry misses 41--45\% of system energy across all three workloads, with non-GPU components accounting for the remainder. In our setup, the sequential agent workload consumes 63$\times$ more system energy per output token than saturated serving, reflecting the absence of batching, context growth across turns, and tool-induced idle periods. Extended thinking generates 21--75\% more tokens per problem, while per-token energy differs by less than 1\% between modes within each dataset, indicating that the resulting increase in energy is driven by output volume rather than a change in per-token efficiency. Continuous batching improves system-level energy efficiency by 3.2$\times$ from 1 to 16 requests per second, as GPU power plateaus while throughput continues to increase with batching depth. In agent and reasoning workloads, energy increases mainly because the model generates more tokens or runs longer as context grows, while system power changes little. For the memory-bandwidth-bound reasoning workload, throughput remains unchanged under per-GPU power caps of 400--600\,W but drops sharply at 300\,W. These findings suggest that context-management techniques such as summarization and selective retrieval may help reduce energy consumption in agent deployments.