Φ-Bench: Can Large Language Models Engineer the Infrastructure That Powers Them?

Hugging Face Daily Papers Papers
llm-benchmark infrastructure-engineering optimization system-design evaluation ai-engineering

Summary

Φ-Bench is a benchmark designed to evaluate large language models on their ability to engineer and optimize the LLM infrastructure stack, covering tasks from kernel optimization to end-to-end system design.

Large language models (LLMs) have demonstrated remarkable capabilities in reasoning and code generation, raising the prospect that they could assist in developing and optimizing the very infrastructure that powers them. However, existing benchmarks mainly focus on isolated kernels, predefined operators, or pre-specified optimization targets, and therefore fail to evaluate the ability of LLMs to perform open-ended, long-horizon LLM infrastructure engineering. To address this gap, we present Φ-Bench, a benchmark for systematically evaluating LLMs on engineering the LLM infrastructure stack. Derived from optimization problems studied in frontier research and grounded in real-world code repositories, Φ-Bench provides broad coverage of the LLM infrastructure stack and spans tasks of varying complexity, ranging from localized kernel-level function completion to long-horizon implementation and end-to-end system optimization. Extensive experiments on frontier LLMs reveal their current capabilities and limitations in engineering complex LLM infrastructure, offering insights into the challenges that remain on the path toward autonomous optimization of future AI infrastructure.
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Abstract

Φ-Bench evaluates large language models on open-ended engineering of the LLM infrastructure stack across tasks from kernel optimization to end-to-end system design.

Large language models (LLMs) have demonstrated remarkable capabilities in reasoning and code generation, raising the prospect that they could assist in developing and optimizing the very infrastructure that powers them. However, existing benchmarks mainly focus on isolated kernels, predefined operators, or pre-specified optimization targets, and therefore fail to evaluate the ability of LLMs to perform open-ended, long-horizon LLM infrastructure engineering. To address this gap, we presentΦ-Bench, a benchmark for systematically evaluating LLMs on engineering theLLM infrastructure stack. Derived from optimization problems studied in frontier research and grounded in real-world code repositories,Φ-Benchprovides broad coverage of theLLM infrastructure stackand spans tasks of varying complexity, ranging from localized kernel-level function completion tolong-horizon implementationandend-to-end system optimization. Extensive experiments on frontier LLMs reveal their current capabilities and limitations in engineering complex LLM infrastructure, offering insights into the challenges that remain on the path towardautonomous optimizationof future AI infrastructure.

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