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Dual-model literary translation pipeline achieves 2-3 books/day on Tesla P40s

By PulseAugur Editorial · [1 sources] ·

A user has detailed a two-model pipeline for literary book translation, utilizing two Tesla P40 GPUs. The pipeline employs Gemma 4 - 26B-A4B for translation at approximately 40 tokens/second and Qwen3.6 35B-A3B for proofreading at 50-70 tokens/second. This setup leverages Multi Token Prediction (MTP) speculative decoding and a 64K context window to achieve efficient, high-volume translation of entire books. AI

IMPACT Demonstrates efficient local LLM deployment for specialized, high-volume tasks like book translation.

RANK_REASON User-developed tool and inference setup details.

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Dual-model literary translation pipeline achieves 2-3 books/day on Tesla P40s

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4 / 100
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Tool
User-developed tool and inference setup details.
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infra, product
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/neowisard ·

    Running a 2-model literary book-translation pipeline on 2x Tesla P40: gemma-4-26B-A4B at ~40 tok/s + Qwen3.6-35B-A3B at 50-70 tok/s with MTP spec decode — full llama-server flags inside

    <!-- SC_OFF --><div class="md"><p><strong>Disclosure up front: I built this tool (open source, &quot;Sunny Narrator&quot;) and I'm the author — this post is about the inference setup, not an ad.</strong> Feel free to skip to the flags if you're here for the numbers.</p> <p>Contex…

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