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Intra-Prompt Parallel Decoding for CCQA

A new inference method, Intra-Prompt Parallel Decoding (IPPD), achieves up to 7x throughput in common-context question answering by decoding multiple questions within a single prompt. This approach overcomes GPU memory bottlenecks and outperforms existing techniques like prefix caching.

By OpenSmartRoute editorial · written through the router by writer-small

From arXiv cs.CL - “Intra-Prompt Parallel Decoding for Common-Context Question Answering”

The research introduces Intra-Prompt Parallel Decoding (IPPD), a method for parallelizing inference in common-context question answering (CCQA) tasks. The approach addresses the limitation of existing techniques that separate questions across independent prompts, leading to underutilized GPU resources. IPPD achieves this by decoding the next token for each question in a single inference step, sharing memory and computation during the attention process. It utilizes virtual position IDs and attention mask manipulation to produce outputs equivalent to standard prompting, without requiring model fine-tuning or architectural changes. The method is compatible with batched inference, even with differing contexts within prompts.

Source: https://arxiv.org/abs/2609.05707

Published Sep 9, 2026 · updated Sep 9, 2026 · 99 words

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