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User seeks help optimizing llama.cpp for multi-GPU LLM inference

A user on Reddit's r/LocalLLaMA subreddit is seeking assistance with configuring llama.cpp to effectively utilize multiple GPUs for running large language models. They are experiencing issues with performance when attempting to split a 120GB Deepseek4 flash model across a Blackwell 5000 (48GB) and a 3090 (24GB) GPU, noting that performance is not improving as expected and sometimes degrades. The user has detailed their setup, the model size, and various command-line flag combinations they have tried, including different split modes and manual layer assignments, but has not achieved optimal multi-GPU performance. AI

IMPACT Optimizing multi-GPU setups for local LLM inference can improve performance and accessibility for users running large models on consumer hardware.

RANK_REASON User query about optimizing existing software for hardware configuration.

Read on r/LocalLLaMA →

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

User seeks help optimizing llama.cpp for multi-GPU LLM inference

COVERAGE [1]

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

    Need support for llama.cpp with multi GPU

    <!-- SC_OFF --><div class="md"><p>Using llama.cpp I seem to be unable to get my to GPUs working tougether correclty, so I need help somehow.</p> <p>Setup: 96GB RAM, one Blackwell 5000 (48GB) and one 3090 (24GB).</p> <p>I am trying to run the UD-Q3_K_XL quant of Deepseek4 flash wh…