InferenceBench: A Benchmark for Open-Ended LLM Inference Optimization by AI Agents
InferenceBench is a new benchmark designed to evaluate AI agents in open-ended LLM inference optimization, moving beyond prescribed workflows to test genuine engineering capabilities. Agents were tasked with optimizing inference speed on an H100 GPU within a two-hour time budget across four distinct scenarios targeting prefill latency, decode latency, throughput, and balanced performance. While frontier agents improved significantly over naive PyTorch baselines (up to 8.08x) and matched default
Analysis
TL;DR
- InferenceBench is a new benchmark designed to evaluate AI agents in open-ended LLM inference optimization, moving beyond prescribed workflows to test genuine engineering capabilities.
- Agents were tasked with optimizing inference speed on an H100 GPU within a two-hour time budget across four distinct scenarios targeting prefill latency, decode latency, throughput, and balanced performance.
- While frontier agents improved significantly over naive PyTorch baselines (up to 8.08x) and matched default vLLM settings (4.05x), they underperformed compared to simple hyperparameter searches (up to 11.53x).
- Qualitative analysis reveals that agents tend to converge on a single inference framework rather than exploring diverse strategies, spending most of their time on re-measurement and hyperparameter tuning instead of architectural exploration.
- The study suggests the primary bottleneck for current agents is not domain knowledge but the ability to propose diverse configurations, evaluate them systematically, and avoid local optima through memorized solutions.
Why It Matters
This benchmark provides a critical evaluation metric for the next generation of autonomous AI researchers, highlighting the gap between theoretical capability and practical engineering execution in real-world constraints. For practitioners, it underscores the limitations of current agent architectures in handling open-ended optimization tasks, suggesting that reliance on known recipes may hinder performance in novel or complex deployment scenarios. Understanding these bottlenecks is essential for developing more robust agents capable of true innovation rather than just efficient replication of existing methods.
Technical Details
- Benchmark Setup: Agents deploy an OpenAI-compatible inference server using one H100 GPU and a target LLM, operating under a strict two-hour wall-clock time budget.
- Optimization Scenarios: Four specific scenarios isolate key inference bottlenecks: prefill latency, decode latency, concurrent request throughput, and a balanced combination of all three.
- Performance Metrics: Agents achieved up to 8.08x speedup over naive PyTorch and 4.05x over default vLLM settings, but failed to match the 11.53x speedup achieved by simple hyperparameter search within the same timeframe.
- Agent Behavior Analysis: Trajectory analysis showed that out of 15 frontier agent configurations, most converged on a single inference framework, testing only a few distinct configurations before spending remaining time on iterative refinement and error repair.
- Core Limitation Identified: The study identifies that agents struggle with proposing diverse structural configurations, indicating that the limitation lies in exploratory capacity and systematic evaluation rather than lack of technical knowledge.
Industry Insight
- Shift from Knowledge to Exploration: AI agent development must prioritize mechanisms for diverse hypothesis generation and systematic evaluation over mere retrieval of best practices to overcome local optima in engineering tasks.
- Hybrid Approaches are Key: Since simple hyperparameter searches outperformed complex agent trajectories, integrating automated search algorithms with agent-driven architectural decisions may yield superior results in inference optimization.
- Benchmarking Real-World Engineering: Future benchmarks should focus on open-ended, resource-constrained engineering problems to better assess an agent's ability to innovate and adapt, rather than just executing predefined pipelines.
Disclaimer: The above content is generated by AI and is for reference only.