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AMD EPYC & ROME servers show strong performance for large language models

By PulseAugur Editorial · [1 sources] ·

A user on Reddit shared performance benchmarks for running large language models on rented server hardware, specifically highlighting the effectiveness of AMD EPYC and ROME motherboards. The tests, which cost around $40, demonstrated that this configuration can achieve nearly linear performance scaling for concurrent streams up to at least 8, even with memory-intensive models like Qwen 3.8 Flash Next Unsloth Q8. AI

IMPACT Suggests specific server hardware configurations can significantly improve LLM inference performance and scalability.

RANK_REASON User-generated benchmark results for hardware configurations running LLMs.

Read on r/LocalLLaMA →

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

AMD EPYC & ROME servers show strong performance for large language models

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Composite score across the factors below. Higher = stronger signal that this story matters right now.
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User-generated benchmark results for hardware configurations running LLMs.
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/sleight42 ·

    Rented some server time and wanted to share some learnings from benching using RAM heavier servers with some decent GPU: AMD EPYC and ROME motherboards really *ARE* worth it: Qwen 3.8 Flash Next Unsloth Q8 at 94 tok/s c=1, 456 tok/s c=8 .

    <!-- SC_OFF --><div class="md"><p>Raw data in the comments, if you go for that sort of thing.</p> <h1>The TL;DR</h1> <p>This build generates a nearly linear performance per stream up to <em>at least</em> 8 streams using a context window of 124k. I didn't test beyond that.</p> <h1…

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