Debugging LLM Inference Performance: Best Practices and Techniques

Описание и применение

Автоматизированный скрипт выявляет узкие места в инференсе LLM и предлагает конкретные исправления, такие как снижение max_tokens, уменьшение температуры и смену модели.

Задача

Identifying and fixing performance bottlenecks in LLM inference calls

Результат

Automated script identifies max_tokens over‑allocation, high temperature, and large model usage as primary bottlenecks, recommending lower max_tokens, reduced temperature, and smaller models for faster inference.

Рабочий процесс

• Capture a slow baseline request and time it
• Serialize the request log into compact JSON
• Define a system prompt for inference performance analysis
• Run diagnostic with a reasoning model (qwen-3-32b)
• Stream the diagnosis for real‑time feedback
• Integrate the script into production or CI
• Optional: add Slack notification or regression test

Доказательства

Captured baseline: 4.82s Diagnosis: - Bottleneck: max_tokens is set to 4000 but completion_tokens is only 312.

Источник