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llama.cpp now loads MTP tensors by default, increasing resource usage

By PulseAugur Editorial · [1 sources] ·

The popular open-source inference engine llama.cpp has updated its default behavior to automatically load Multi Token Prediction (MTP) tensors. This change affects models that have MTP tensors baked in, such as GLM-5.2, hy_v3, qwen35moe, and step35. Previously, these tensors were only loaded if speculative decoding was explicitly enabled. Now, they will be loaded by default, potentially increasing VRAM and RAM usage for users even if they do not utilize MTP functionality. AI

IMPACT Users of llama.cpp with MTP-enabled models may see increased VRAM/RAM usage due to the default loading of MTP tensors.

RANK_REASON This is a change in default behavior for an open-source inference engine, affecting resource usage for specific model types.

Read on r/LocalLLaMA →

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

llama.cpp now loads MTP tensors by default, increasing resource usage

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This is a change in default behavior for an open-source inference engine, affecting resource usage for specific model types.
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/Shoddy_Bed3240 ·

    PSA: llama.cpp now loads MTP tensors by default for any draft-mtp arch, even with MTP disabled

    <!-- SC_OFF --><div class="md"><p>If your GGUF has MTP/NextN tensors baked in (GLM-5.2, hy_v3, qwen35moe, step35, etc.), recent llama.cpp builds load them by default — even if you never pass <code>--spec-type draft-mtp</code>. Before, they were skipped unless you actually enabled…

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