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Users seek hardware advice for faster Qwen3.6 35B model inference

By PulseAugur Editorial · [1 sources] ·

A user on Reddit is seeking hardware configurations to achieve high inference speeds with the Qwen3.6 35B model. They are currently experiencing around 270-300 tokens/second for prefill and 30 tokens/second for decode on their AMD RX6600XT and Ryzen 7 5700X setup. The user notes that existing online benchmarks are inaccurate for their hardware and is looking for advice from others who have achieved faster performance, specifically aiming for 1000+ prefill and 100+ decode tokens/second. AI

IMPACT Provides insights into real-world hardware performance for running large language models locally.

RANK_REASON User discussion about hardware performance for a specific model, not a new release or significant industry event.

Read on r/LocalLLaMA →

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

Users seek hardware advice for faster Qwen3.6 35B model inference

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0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
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Commentary
User discussion about hardware performance for a specific model, not a new release or significant industry event.
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Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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High
Clearly on-topic for AI-industry coverage.
Story freshness
57 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

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COVERAGE [1]

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

    Those in the 1000+ prefill and 100+ decode range on Qwen3.6 35B at Q4, what hardware are you running?

    <!-- SC_OFF --><div class="md"><p>Trying to see what I can scrounge together bare minimum hardware requirements to get up to that rough speed. </p> <p>Right now I'm running an RX6600XT and Ryzen 7 5700X with 32GB of DDR4 at 3600MHZ. CachyOS, vanilla llama.cpp built with ROCm and …

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