Agentic LLM Inference Parameters Reference for Qwen and Gemma

This page is a practical reference for agentic LLM inference tuning (temperature, top_p, top_k, penalties, and how they interact in multi-step and tool-heavy workflows). It sits alongside the broader LLM performance engineering hub and matches best with a clear LLM hosting and serving story—throughput and scheduling still dominate when the model is starved, but unstable sampling burns retries and output tokens before the GPU does. This page consolidates: vendor recommended parameters embedde...