LLM Profiler: A Lightweight Performance Analysis Tool for Large Language Model Inference

This post introduces LLM Profiler, a lightweight performance analysis tool designed specifically for large language model (LLM) inference scenarios. It supports dual analysis at both system and model levels, helping developers quickly locate performance bottlenecks and optimize inference efficiency. Key features include low overhead, plug-and-play integration, multi-backend support (PyTorch, TensorFlow, etc.), and visual output (flame graphs, timing charts). The tool is maintained by tuxedo-feynman and hosted on GitHub (link: https://github.com/tuxedo-feynman/llm-profiler), released on 2026-06-13.

LLM Profiler fills the gap in the field of LLM inference performance analysis tools. It is a lightweight tool focused on LLM inference scenarios, capable of collecting key metrics at both system and model levels during inference. The project is maintained by tuxedo-feynman and released on GitHub on 2026-06-13. Its core goal is to help developers quickly identify performance bottlenecks and optimize inference efficiency.

LLM Profiler provides two main levels of analysis: System-level monitoring: Tracks CPU utilization, memory usage (including potential leaks), GPU memory (peak usage and fragmentation for CUDA devices), and I/O latency (disk/network delays during model loading and data transfer). Model-level profiling: Records per-layer forward propagation time (to find hotspots), analyzes Self-Attention/Cross-Attention performance, evaluates KV Cache hit rate, and calculates real-time token generation rate (tokens/second).

The tool is useful in several scenarios:

1. Model selection comparison: Benchmark candidate models on the same hardware to make scientific decisions.
2. Deployment environment evaluation: Assess target machine's capacity before production to avoid online failures.
3. Performance regression detection: Integrate into CI/CD to detect performance degradation after model/code updates.
4. Quantization/distillation validation: Verify optimization effects of quantized/distilled models and monitor accuracy-loss impact on speed.

LLM Profiler has several technical highlights:

1. Low overhead: Uses sampling instead of full recording to minimize impact on inference.
2. Plug-and-play: No model code modification needed; uses wrapper pattern for transparent injection of performance collection logic.
3. Multi-backend support: Compatible with PyTorch, TensorFlow, Transformers, vLLM, etc.
4. Visual output: Generates intuitive flame graphs and timing charts for easy data interpretation.

LLM Profiler combines system monitoring and model profiling with minimal invasiveness, providing comprehensive performance insights. It helps teams optimize LLM inference efficiency and reduce operational costs in both local debugging and cloud deployment. Recommendations: Use it for model selection, deployment evaluation, CI/CD integration, and validation of optimized models (quantization/distillation) to ensure performance and quality.