# Parallel Prompting speeds up multi‑question LLM inference

> The new method reads a shared document once per decode step, cutting query time by up to six times on Nvidia GPUs.

Oossa · 2026-11-13 · https://oossa.com/en/parallel-prompting-speeds-up-multi-question-llm-inference

Researchers Zekun Zhao and team introduced Parallel Prompting, a way to answer many questions about the same long text in one go. It treats each question as a separate stream within a single request, so the model reads the document only once per step. Implemented in the SGLang system with a few dozen lines of code, the technique runs on CodeLlama‑7B and shows large speed gains. On an Nvidia A100 GPU it is 3.7 × faster, and on an RTX A6000 it is about 6 × faster than the standard SGLang setup.

## The facts

- 3.7 × faster on A100, 5.9‑6.0 × faster on RTX A6000
- Works with 64‑256 questions per 8k‑16k token document

## Why it matters

It lets developers and data‑labeling teams process many queries per document much quicker, reducing GPU costs.

## Sources & references

1. [Read Once, Answer Many: Parallel Prompting for Shared-Context LLM Inference](https://arxiv.org/abs/2611.15130) – arXiv, 2026-11-13

Last updated: 2026-11-13