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llama.cpp pipeline parallelism wastes VRAM, user finds

A user discovered that the default pipeline parallelism in llama.cpp may be wasting VRAM without providing any speed benefits. By compiling llama.cpp with the flag -DGGML_SCHED_MAX_COPIES=1, users can avoid this unnecessary VRAM allocation. This optimization is particularly relevant when all model layers are offloaded to the GPU. AI

IMPACT Users can reclaim VRAM by disabling default pipeline parallelism in llama.cpp, potentially allowing for larger models or contexts.

RANK_REASON User-discovered optimization for an open-source software project. [lever_c_demoted from research: ic=1 ai=0.7]

Read on r/LocalLLaMA →

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

COVERAGE [1]

  1. r/LocalLLaMA TIER_1 (TL) · /u/Warrenio ·

    Pipeline parallelism in llama.cpp may be wasting your VRAM

    <!-- SC_OFF --><div class="md"><p>By default, llama.cpp enables pipeline parallelism, presumably to speed up inference. In my testing, I found that pipeline parallelism has no speed benefit and comes at a significant cost of VRAM.</p> <p>This cost can be avoided by compiling llam…