Individual skill
groq-performance-tuning
Optimize Groq API performance with model selection, caching, streaming, and parallel requests. Use when experiencing slow responses, implementing caching strategies, or optimizing request throughput for Groq integrations. Trigger with phrases like "groq performance", "optimize groq", "groq latency", "groq caching", "groq slow", "groq speed".
skills/.curated/groq-performance-tuning/SKILL.md
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SKILL.mdcore · 7,821 bytesreferences/examples.mddoc · 2,713 bytesreferences/implementation.mddoc · 5,402 bytes
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# Groq Performance Tuning
## Overview
Maximize Groq's LPU inference speed advantage. Groq already delivers extreme throughput (280-560 tok/s) and low latency (<200ms TTFT), but client-side optimization -- model selection, prompt size, streaming, caching, and parallelism -- determines whether your application fully exploits that speed.
This skill walks through six tuning levers at a high level; the complete, copy-pasteable code for each lives in [references/implementation.md](references/implementation.md), and end-to-end worked scenarios live in [references/examples.md](references/examples.md).
## Prerequisites
- **Groq API key** — set `GROQ_API_KEY` in the environment. The `groq-sdk` client (`new Groq()`) reads it automatically; never hardcode the key.
- **Node.js 18+** with the `groq-sdk` package installed (`npm install groq-sdk`).
- Optional packages for the caching and parallelism steps: `lru-cache` and `p-queue` (`npm install lru-cache p-queue`).
- A baseline latency measurement of your current integration so you can confirm the tuning actually helps.
