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) ·
AWQ W4A16: wtdcode/Qwen3.8-Flash-Next-AWQ-W4A16 (1) ·
Maya GGUF (Maya-S 340 GB or Maya-XS 277 GB; Engram tables stay on SSD) (1) ·
Official checkpoint: dense MXFP8 through an SM80 Marlin W8A16 path, routed experts MXFP4 through W4A16; KV FP8 (1) ·
W4A16 AutoRound: Intel/Qwen3.8-Flash-Next-W4A16-AutoRound; 95.4 GiB BF16 PLE table streamed from NVMe; KV fp8 e4m3 (software-decoded on SM80) (1)
Custom SM80 vLLM (patched from public commit 79a7108d9) (1) ·
Project Maya (Strata and llama.cpp lineage) (1) ·
vLLM nightly + PR #53899 (a5530b9) + the repo's patch scripts (1) ·
vLLM nightly 7d0b4e57 plus the author's pure-Python fork clee/vllm-sm80 (branch q38fn/stack) (1) ·
wtdcode/vllm-backport (image lazymio/vllm-backport:latest-sm80) (1)
https://discord.com/channels/1518634379604136178/1528826330454429706/1560602001908170753 (1) ·
https://github.com/clee/qwen3.8-flash-next-3x170hx (1) ·
https://github.com/mw00/project-maya#how-fast-is-it (1) ·
https://github.com/vektorprime/qwen38-flash-next-pp2 (1) ·
https://github.com/wgkbfcp/deepseek-v4.1-flash-1017-cmp170hx-recipe (1)
Showing below up to 5 results in range #1 to #5.


