Observability & Evaluation · Software component

Inference Engine Profiler

Software componentObservability & EvaluationObservability & Evaluationarc:InferenceEngineProfiler

A profiler that breaks down LLM inference-engine execution by operation class, such as attention kernels, KV-cache access and quantized layers, to locate model-level bottlenecks.

Responsibility. Attributes LLM inference time to engine operation classes.

Also known as: Inference-specific profiler, LLM engine profiler, Per-layer engine profile (dumpProfile)

Variant of Performance Profiler abstract

When to choose. Choose when system profiling shows inference dominates and the time split across attention kernels, KV-cache access, quantized operations and batching must be understood.

Direct neighbourhood (hover for relationship types)

Relationships

monitors assurance

Attention Kernel abstract Ch4.4
Inference Server Ch4.4
KV Cache Manager Ch4.4
Optimized Inference Engine abstract Ch7.4 Ref7.01

alternative to variability

Quantitative guidance

As stated by the sources; verify before use.

Legal-contract workload: attention computation accounted for 77.2% of inference latency (Ch4.4).
Attention typically consumes 60-80% of inference time for typical sequence lengths (Ch4.4).

Classification

Technologies: TensorRT-LLM profilingtrtexec --dumpProfileNsight Deep Learning Designer
Quality attributes: Maintainability (ISO/IEC 25010)Performance efficiency (ISO/IEC 25010)

Sources

Ch4.4: T. Nguyen, "Performance Profiling and Optimization," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.4. ISBN: 9798244538229.
Ch7.4: T. Nguyen, "TensorRT-LLM Fundamentals and Quantization," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 7.4. ISBN: 9798244538229.
Ref7.01: NVIDIA, "Best practices," NVIDIA TensorRT Documentation. Accessed: Sep. 27, 2026. [Online]. Available: https://docs.nvidia.com/deeplearning/tensorrt/latest/performance/best-practices.html