The Need for Automated Inference Optimization
As LLM deployments scale, the complexity of optimizing inference—balancing latency, throughput, cost, and quality—has outpaced manual engineering capabilities. InferenceBench introduces a systematic approach to evaluating how AI agents can autonomously navigate this optimization space. Rather than relying on static benchmarks, it focuses on the agent's ability to perform open-ended tasks like model quantization, speculative decoding configuration, and cache management in dynamic environments.
Evaluating Agentic Performance in Optimization
InferenceBench shifts the focus from model performance to agent performance. It provides a testbed where agents are tasked with optimizing inference pipelines under specific constraints. The benchmark measures:
- Optimization Efficiency: How quickly an agent arrives at a configuration that meets performance targets.
- Constraint Adherence: The ability to maintain output quality (e.g., perplexity or task-specific accuracy) while aggressively optimizing for speed or cost.
- Adaptability: How well the agent handles changing workloads or hardware constraints, simulating real-world production shifts.
By framing inference optimization as an agentic task, the benchmark highlights the gap between current LLM reasoning capabilities and the technical precision required for infrastructure-level decision making. It serves as a critical tool for developers looking to automate the "knob-turning" process in high-scale AI deployments.