Observability & Evaluation · Software component
Inference Performance Analyzer
Software componentObservability & EvaluationObservability & Evaluationarc:InferencePerformanceAnalyzer
A benchmarking tool that drives load against an inference server to compare configurations and measure throughput and latency before and after optimisation.
Responsibility. Benchmarks inference serving configurations empirically.
Also known as: Performance Analyzer, Load-driven latency/throughput measurement, Load tester, Production validation load test, Inference benchmark, Kernel profiler, DGX Cloud Benchmarking suite, Performance Explorer, trtexec benchmark
Relationships
invokes dependency
is invoked by dependency
reads dependency
evaluates assurance
- GPU Node abstract Ref4.02
- Inference Server Ch4.5 Ref2.01 +2
- LLM Inference Service Ch4.5
- Optimized Inference Engine abstract Ch4.6 Ch7.4 +3
produces lifecycle
Design guidance
- SHOULD be used to validate every optimisation, since benefits vary by model architecture and hardware.
- SHOULD validate with realistic concurrent-user load that autoscaling responds and P95 latency stays within target at the target throughput before production.
- SHOULD compare latency percentiles and throughput across alternative batching configurations of the same model.
- SHOULD benchmark one variable at a time from a baseline, with 5-10 runs and warm-up periods, using realistic batch sizes and sequence lengths.
- SHOULD report time-to-first-token, time per token, end-to-end generation time, throughput and GPU memory utilisation.
- SHOULD measure baseline performance before optimizing and evaluate cluster size, precision, framework and parallelism strategy for TCO.
- SHOULD run a warm-up period before measurement so GPU clocks and caches reach steady state.
- SHOULD measure with and without host-device transfers to separate compute-bound from memory-bound behaviour (Ref7.01).
- SHOULD lock GPU clocks for deterministic measurements (Ref7.01).
- SHOULD measure throughput (inferences/s), average and P95/P99 latency and GPU utilization at target concurrency before and after each batching change.
Quantitative guidance
As stated by the sources; verify before use.
- Llama 3 70B training on 1T tokens: 115.4 days to 3.8 days (97% less time) for a 2.6% cost increase when scaled out (Ref4.02).
- NeMo Framework software improvements yielded a 25% platform performance increase in 2024 (Ref4.02).
- Example benchmark: 100 warm-up iterations then 10-second measurement (Ch7.4, Ref7.01).
Classification
- Patterns
- Empirical configuration benchmarking
- Technologies
- Triton Performance AnalyzerLocustk6NVIDIA Nsight SystemsNVIDIA DGX Cloud Benchmarkingtrtexecperf_analyzer
- Quality attributes
- Performance efficiency (ISO/IEC 25010)
- Risks mitigated
- Unvalidated optimisation assumptions
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.
- Ch4.5: T. Nguyen, "NVIDIA NIM and Triton Inference Server," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.5. ISBN: 9798244538229.
- Ch4.6: T. Nguyen, "TensorRT-LLM and NVIDIA Fleet Command," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.6. 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.
- Ref2.01: NVIDIA, "Optimization," NVIDIA Triton Inference Server User Guide. Accessed: Sep. 26, 2026. [Online]. Available: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/user_guide/optimization.html
- Ref4.01: NVIDIA, "TensorRT-LLM," GitHub repository. Accessed: Sep. 27, 2026. [Online]. Available: https://github.com/NVIDIA/TensorRT-LLM
- Ref4.02: E. Potyraj, "Measure and Improve AI Workload Performance with NVIDIA DGX Cloud Benchmarking," NVIDIA Technical Blog, Mar. 18, 2025. [Online]. Available: https://developer.nvidia.com/blog/measure-and-improve-ai-workload-performance-with-nvidia-dgx-cloud-benchmarking/
- 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
- Ref7.02: NVIDIA, "Batchers," NVIDIA Triton Inference Server User Guide. Accessed: Sep. 27, 2026. [Online]. Available: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/user_guide/batcher.html
- Ref7.05: S. Verma and N. Vaidya, "Mastering LLM Techniques: Inference Optimization," NVIDIA Technical Blog, Nov. 17, 2023. [Online]. Available: https://developer.nvidia.com/blog/mastering-llm-techniques-inference-optimization/