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Qwen 3.6 MTP models benchmarked for speculative decoding performance

Performance benchmarks for Qwen 3.6 models, specifically the 27B and 35B MTP variants, have been released. The tests focused on speculative decoding within the llama.cpp framework, utilizing an RTX 4080 16GB GPU. Key metrics evaluated included token speed, VRAM consumption, and the optimal settings for the --spec-draft-n-max parameter. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Provides performance data for Qwen 3.6 models, aiding operators in hardware and software configuration choices.

RANK_REASON Benchmark results for specific model variants and their performance with a particular software framework. [lever_c_demoted from research: ic=1 ai=1.0]

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COVERAGE [1]

  1. Mastodon — fosstodon.org TIER_1 · [email protected] ·

    Benchmark results for Qwen 3.6 27B and 35B MTP speculative decoding in llama.cpp on RTX 4080 16GB. Token speed, VRAM cost, and optimal --spec-draft-n-max settin

    Benchmark results for Qwen 3.6 27B and 35B MTP speculative decoding in llama.cpp on RTX 4080 16GB. Token speed, VRAM cost, and optimal --spec-draft-n-max settings. # SelfHosting # LLM # AI # llama .cpp # NVidia # Hardware https://www. glukhov.org/llm-performance/be nchmarks/compa…