Drilldown: Recipe
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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) ·
Maya GGUF (Maya-S 340 GB or Maya-XS 277 GB; Engram tables stay on SSD) (1) ·
Native MXFP4 experts + FP8 attention (official deepseek-ai checkpoint, about 155.4 GiB); KV cache fp8 (1) ·
W4A16, about 4.3 bpw, 163 GiB on disk (1)
ExLlamaV3 1.5.4 (built for SM80) with a small OpenAI-compatible server (1) ·
Morrowmake/vllm-cmp170hx (via the glm53-flash-cmp170hx-recipe) (1) ·
Project Maya (Strata and llama.cpp lineage) (1) ·
vLLM nightly + PR #53899 (a5530b9) + the repo's patch scripts (1) ·
vLLM: haosdent/vllm branch dsv4-flash-a100 with a vendored copy of allover326's 7 patches (image dsv4-a100:devel) (1)
https://discord.com/channels/1518634379604136178/1518695289278566450/1561743826945843304 (1) ·
https://github.com/Flun/glm53-flash-cmp170hx-exl3 (1) ·
https://github.com/mw00/project-maya#how-fast-is-it (1) ·
https://github.com/tim-odonnell/cmp170hx-deepseek-v4-flash (1) ·
https://github.com/vektorprime/qwen38-flash-next-pp2 (1)
Showing below up to 5 results in range #1 to #5.

