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User doubles LLM inference speed by fixing PCIe slot bottleneck

A user building a multi-GPU setup for local LLM inference discovered a significant performance bottleneck caused by a misconfigured PCIe slot. One of the four RTX 3090 GPUs was incorrectly placed in a slot that only supported PCIe 2.0 x4 speeds, severely limiting its bandwidth. After reconfiguring the GPUs to utilize their full PCIe capabilities, the user observed a dramatic increase in inference speeds, with Mistral 128B performance nearly doubling. AI

IMPACT Fixing hardware bottlenecks can significantly improve local LLM inference speeds and efficiency.

RANK_REASON User-generated troubleshooting guide for hardware configuration impacting LLM performance.

Read on r/LocalLLaMA →

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

COVERAGE [1]

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

    I accidentally crippled my 4x RTX 3090 LLM rig with a hidden PCIe 2.0 x4 slot and fixing it doubled Mistral 128B performance

    <!-- SC_OFF --><div class="md"><p>I’m posting this as a warning for anyone building multi-GPU local LLM rigs with older workstation/HEDT boards.</p> <p>My setup (Node #04)</p> <ul> <li>Gigabyte X399 Designare EX</li> <li>Threadripper 1950X</li> <li>128GB DDR4</li> <li>4x RTX 3090…