Drilldown: Recipe
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DeepSeek V4.1 Flash on 2x 8 GB (Project Maya) (1) ·
GLM 5.3 Flash on 2x 8 GB (ExLlamaV3 layer split) (1) ·
GLM 5.3 Flash on 2x 8 GB (Project Maya) (1) ·
Qwen3.8 Flash Next on 2x 8 GB (Morrowmake EXL3) (1) ·
Qwen3.8 Flash Next on 2x 8 GB (Plean PP2) (1) ·
Qwen3.8 Flash Next on 2x 8 GB (vektorprime PP2) (1)
AWQ INT4: cyankiwi/Qwen3.8-Flash-Next-AWQ-INT4; 95 GiB BF16 PLE table in host RAM (FP8 table optional) (1) ·
EXL3 3.05 bpw: turboderp/GLM-5.3-Flash-exl3 (1) ·
EXL3 5.05 bpw; PLE (n-gram) table read from SSD (1) ·
Maya GGUF (Maya-S 340 GB or Maya-XS 277 GB; Engram tables stay on SSD) (1) ·
Maya-M GGUF (116 GB), every expert in VRAM; Maya-S (96.5 GB) and Maya-S24 (94.7 GB) also reported (1) ·
Mixed INT4 AutoRound (Minachist/Qwen3.8-Flash-Next-INT4-Mixed-AutoRound, about 65 GB) with the full BF16 PLE table on NVMe (1)
ExLlamaV3 1.5.4 (built for SM80) with a small OpenAI-compatible server (1) ·
Morrowmake/vllm-cmp170hx (EXL3 path) (1) ·
Project Maya (Strata and llama.cpp lineage) (1) ·
Project Maya (Strata and llama.cpp lineage, EXL3 loading, DFlash2 drafter) (1) ·
vLLM 0.29.1rc1 nightly plus the checkpoint author's patches (1) ·
vLLM nightly + PR #53899 (a5530b9) + the repo's patch scripts (1)
https://github.com/Flun/glm53-flash-cmp170hx-exl3 (1) ·
https://github.com/Morrowmake/qwen38-flash-next-cmp170hx-recipe (1) ·
https://github.com/mw00/project-maya#how-fast-is-it (2) ·
https://github.com/vektorprime/qwen38-flash-next-pp2 (1) ·
https://www.bozocentral.com/2x-cmp170hx-qwen3-flash-next (1)
Showing below up to 6 results in range #1 to #6.


