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Qwen3.8-27B model sees 2x speedup with Multi-Token Prediction

By PulseAugur Editorial · [1 sources] ·

A benchmark test of Multi-Token Prediction (MTP) on the Qwen3.8–27B-UD-Q4 model has demonstrated a significant speed increase, nearly doubling inference performance on an RTX 4090. The study found that a draft depth of 5 tokens offers the optimal balance between speed and efficiency, with higher values leading to performance degradation. MTP works by having a smaller model draft candidate tokens, which are then verified by the main model, effectively allowing multiple tokens to be processed in a single forward pass without compromising output quality. AI

IMPACT Demonstrates a practical method to double inference speed for large language models, potentially reducing operational costs.

RANK_REASON Benchmark analysis of an existing model's performance with a specific technique. [lever_c_demoted from research: ic=1 ai=1.0]

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

Qwen3.8-27B model sees 2x speedup with Multi-Token Prediction

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Benchmark analysis of an existing model's performance with a specific technique. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Towards AI TIER_1 English(EN) · Alexander Salazar ·

    I Benchmarked Every MTP Depth on Qwen3.8–27B-UD-Q4. Here You Are.

    <h3>I Benchmarked Every MTP Depth on Qwen3.8–27B-UD-Q4_K_M/XL. Here You Are.</h3><h4>Normal decoding is one forward pass per-token. That is the entire bottleneck of local inference.</h4><h3>TL;DR</h3><p>MTP on Qwen3.8–27B-UD-Q4 gives a genuine ~2× on a 4090: 48.5 → 96.2 t/s on K_…

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