RoofLang: Enabling AI-Driven Architecting of LLM Inference Systems

2026-09-11

AI Summary

本文提出RoofLang语言，通过提供通用工作负载表示、可验证变异空间和独立于实现的评估器，解决现有AI优化方法在大模型推理系统架构上的局限性。

Abstract

AI is beginning to make substantive contributions to LLM inference optimization. Existing AI optimizations are predominantly profiling-based. Profiling-bound feedback confines the search to the capabilities and performance of an existing software stack, preventing a fundamentally better architecture of LLM inference systems from being identified. To enable the AI-driven LLM inference system architecting loop, we argue that a general workload representation, a verifiable mutation space, and an implementation-independent evaluator are required. We present the RoofLang domain-specific language (DSL) that provides these features. In our evaluation, RoofLang reveals that DeepSeek V4-series models could achieve 3.5-39.5$\times$ higher peak decode throughput than other representative models. This gap is disproportionate to their total parameter counts and arises largely from compact KV-cache designs that support larger batches and reduce memory traffic. A persistent optimizer agent further discovered several new architectures that improved both throughput and interactivity of DeepSeek V4 Pro on NVIDIA B300 by 6.23-50.1%.

Problem

• AI-Driven
• LLM Inference Systems
• Profiling-based Optimization
• Architecture Improvement

Innovation

• RoofLang
• AI-Driven Architecting
• LLM Inference Systems
• Verifiable Mutation Space
• Implementation-Independent Evaluator