MLPerf Inference v6.1 adds agentic tests

MLCommons’ new MLPerf Inference v6.1 adds two agentic workloads and reports a 5.7× best per-accelerator gain on its DeepSeek R1 test versus v5.1 one year earlier, the benchmark consortium says in its results announcement. The release records 30 submitting organizations, adds end-to-end RAG and edge-agentic tests, and says more than half used its new API-centric harness. Builders should treat this as a procurement ruler, not a universal model leaderboard: the archive’s token-routing economics analysis shows why throughput only matters after workload and spend are measured.