Llama.cpp adaptive speculation boosts inference speed by up to 50%
Dutchnamn · reddit · 2026-08-25
A fork of Llama.cpp introduces 'adaptive speculation' to optimize inference for dense models like Qwen3.8. Unlike fixed settings for MTP and DFlash, this feature allows setting min/max values, letting the engine auto-adjust the number of suggested tokens based on content type. Benchmarks on a Strix Halo show significant gains for structured content, with generation speed increasing from 44 t/s to 65 t/s—a 50% improvement over the mainline version.
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