ProfInfer: An eBPF-based Fine-Grained LLM Inference Profiler

• URL: http://arxiv.org/abs/2601.20755v2
• Date: Thu, 29 Jan 2026 10:43:56 GMT
• Title: ProfInfer: An eBPF-based Fine-Grained LLM Inference Profiler
• Authors: Bohua Zou, Debayan Roy, Dhimankumar Yogesh Airao, Weihao Xu, Binqi Sun, Yutao Liu, Haibo Chen,
• Abstract summary: We develop a fine-grained, non-intrusive profiling framework for modern inference engines.<n>Our system attaches probes to runtime functions across multiple layers -- without modifying or recompiling the source.<n>It transforms collected traces into rich visualizations of operators, graphs, timelines, and hardware counter trends.
• Score: 4.191309912359899
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As large language models (LLMs) move from research to production, understanding how inference engines behave in real time has become both essential and elusive. Unlike general-purpose engines such as ONNX Runtime, today's LLM inference systems offer little operator-level visibility, leaving developers blind to where time and resources go. Even basic questions -- is this workload memory-bound or compute-bound? -- often remain unanswered. To close this gap, we develop a fine-grained, non-intrusive profiling framework for modern LLM inference engines, exemplified by llama-cpp but applicable to similar runtime architectures. Built on extended Berkeley Packet Filter (eBPF) technology, our system dynamically attaches probes to runtime functions across multiple layers -- without modifying or recompiling the source. It transforms collected traces into rich visualizations of operators, graphs, timelines, and hardware counter trends, exposing how dense inference, Mixture-of-Experts routing, and operator offloading behave in practice. With less than 4% runtime overhead and high profiling fidelity, our framework makes LLM inference both transparent and diagnosable, turning performance profiling into a practical tool for optimization, scheduling, and resource-aware deployment.

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