Qwen3.8-Flash-Next MTP merged in ik_llama.cpp (integrated head or separate -md file)... 45 → 90 tok/s on a 5090 + 128GB, works down to a 12GB 4070
Mirrored from r/LocalLLaMA for archival readability. Support the source by reading on the original site.
| ik_llama.cpp merged qwen4exp MTP support yesterday (PR #2369, mine, reviewed and tested by four other people on their own hardware). It's on main now, no fork or patch needed. Posting since the last couple threads had people saying MTP for this model only exists as an unsloth fork PR... there's another path. Flash-Next ships a 2.6B MTP head that the public converters were dropping. With it loaded the model drafts its own next tokens and then verifies them, so output is identical to running without it. On code I get 93-99% draft acceptance, prose more like 60-65%. Numbers, decode tok/s, no MTP → MTP. My 5090 + 128GB DDR5, experts on CPU: 45 → 90 on coding traffic with ngram-mod chained in front. treo on an RTX Pro 6000: 85 → 113 on code, but story went 83 → 59, so not a free win on prose yet. joelfarthing on a 12GB 4070: 9.5 → 12.5 on code at n_max=1. Caveats: single slot for now (-np 1), and --jinja lowers acceptance because the template turns thinking on by default and reasoning text drafts like prose. Stock CUDA build, then: Already have an unsloth or other quant? The separate head route works on the same code, no re-pull: PR: https://github.com/ikawrakow/ik_llama.cpp/pull/2369 My integrated-head MXFP4 files: https://huggingface.co/jamesrogers/Qwen3.8-Flash-Next-MTP-MXFP4-GGUF ji-farthing's ik_llama KT quants + head: https://huggingface.co/ji-farthing/Qwen3.8-Flash-Next-ik-llama-GGUF Curious what you measure, especially anything AMD!! [link] [comments] |
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