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LLM user seeks faster prompt processing for long agentic runs

By PulseAugur Editorial · [1 sources] ·

A user on the r/LocalLLaMA subreddit is seeking methods to improve prompt processing speed for large language models, specifically mentioning issues with Qwen and a significant drop in tokens per second as context length increases. They are currently using Linux with Vulkan and note that while HIP offers a speed boost, it comes with increased memory usage and poor token generation. The user is looking for solutions to maintain higher processing speeds during long agentic runs. AI

RANK_REASON User-generated question on a niche subreddit about optimizing local LLM performance.

Read on r/LocalLLaMA →

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

LLM user seeks faster prompt processing for long agentic runs

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User-generated question on a niche subreddit about optimizing local LLM performance.
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infra, other
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111 days old
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COVERAGE [1]

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

    How do you increase prompt processing speed ?

    <!-- SC_OFF --><div class="md"><p>I am rocking Qwen like we all know, at 24GB 7900XTX 230k context, but it starts at 850t/s and then lowers to 350t/s when its at 160k context prefill speed, which is frustrating me for my long agentic runs.</p> <p>What is there to be done in order…

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