AA-AgentPerf
Mis à jour le 2025-12-30Confiance : high
aa-agentperfartificial-analysisagentic-inferencebenchmarkagents-per-megawattkv-cache-reusespeculative-decodingprefill-decode-disaggregationproduction-optimizationdeepseek-v4-progb300b300hopperamdpower-efficiencylong-horizon-trajectoriesinfrastructure-evaluation
Advanced benchmark developed by artificial-analysis specifically designed to evaluate agentic inference performance using long-horizon coding trajectories with production-level optimizations. Represents a significant shift from traditional throughput metrics to power-normalized deployable agent capabilities.
Key Innovation
Agents per Megawatt
Primary metric that measures power-normalized agent throughput rather than raw tokens per second:
- Focus: Real-world deployment efficiency
- Scope: Long-horizon coding tasks requiring multiple interaction rounds
- Optimization: Production-ready inference techniques
Technical Features
Production Optimizations
- KV cache reuse: Efficient memory management for extended conversations
- Speculative decoding: Faster generation through prediction lookahead
- Prefill/decode disaggregation: Separated processing stages for optimal resource utilization
Evaluation Methodology
- Long-horizon trajectories: Multi-step coding tasks requiring sustained agent behavior
- Real-world scenarios: Tasks representative of actual deployment use cases
- Hardware-aware metrics: Power consumption integrated into performance measurement
Early Results (June 2026)
Hardware Performance
DeepSeek V4 Pro testing showed:
- GB300 and B300: Superior agents-per-megawatt performance
- Hopper architecture: Lower efficiency in tested configurations
- AMD systems: Competitive but trailing NVIDIA solutions
Industry Significance
Paradigm Shift
AA-AgentPerf represents evolution from academic benchmarking toward practical deployment metrics:
- Beyond TPS: Power-normalized rather than raw throughput focus
- Agent-centric: Long-horizon behavior rather than single-turn generation
- Production-ready: Incorporates real deployment optimizations
Infrastructure Impact
The benchmark addresses critical concerns for production AI deployment:
- Cost optimization: Power efficiency directly impacts operational expenses
- Scalability: Agent-per-watt metrics inform capacity planning
- Hardware selection: Guides infrastructure investment decisions
See also
- artificial-analysis
- Agentic Inference
- Power Efficiency
- Production Optimization