Drilldown: Recipe
More actions
Use the filters below to narrow your results.
DeepSeek V4.1 Flash heretic on 4x 8 GB (1) ·
DeepSeek V4.1 Flash on 6x 8 GB (Deepinfer-SM80 TP2 x PP3) (1) ·
DeepSeek V4.1 Flash on 6x 8 GB (TP6) (1) ·
DeepSeek V4.1 Flash on 8x 8 GB (Deepinfer-SM80 TP4 x PP2) (1) ·
GLM 5.3 Flash on 2x 8 GB (ExLlamaV3 layer split) (1) ·
GLM 5.3 Flash on 2x 8 GB (Project Maya) (1) ·
GLM 5.3 Flash on 3x 8 GB (1) ·
Qwen3.8 Flash Next on 2x 8 GB (Morrowmake EXL3) (1) ·
Qwen3.8 Flash Next on 2x 8 GB (vektorprime PP2) (1) ·
Qwen3.8 Flash Next on 3x 8 GB (clee PP3) (1) ·
Qwen3.8 Flash Next on 4x 8 GB (1)
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) ·
EXL3 3.05 bpw: turboderp/GLM-5.3-Flash-exl3 (1) ·
EXL3 5.05 bpw; PLE (n-gram) table read from SSD (1) ·
Hybrid: routed experts EXL3 2.9 bpw (LibertAIDAI), everything else official FP8/BF16 with the 38 heretic attn.wo_b tensors (1) ·
Maya-M GGUF (116 GB), every expert in VRAM; Maya-S (96.5 GB) and Maya-S24 (94.7 GB) also reported (1) ·
Official checkpoint (FP8 dense, FP4 experts); software FP8 on sm_80 (2) ·
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) ·
W4A16, about 4.3 bpw, 163 GiB on disk (1)
Custom SM80 vLLM (patched from public commit 79a7108d9) (1) ·
ExLlamaV3 1.5.4 (built for SM80) with a small OpenAI-compatible server (1) ·
Morrowmake/vllm-cmp170hx (EXL3 path) (1) ·
Morrowmake/vllm-cmp170hx (via the glm53-flash-cmp170hx-recipe) (1) ·
Morrowmake/vllm-cmp170hx (vLLM fork, container image) (1) ·
Project Maya (Strata and llama.cpp lineage, EXL3 loading, DFlash2 drafter) (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) ·
vLLM PR #56201 head (79a7108d) plus the two Deepinfer-SM80 patches and 30 drop-in modules (2) ·
wtdcode/vllm-backport (image lazymio/vllm-backport:latest-sm80) (1)
engine-managed split with expert tiering across VRAM, RAM and NVMe (1) ·
layer split (no tensor parallel), no P2P (1) ·
PP2 with PLE CPU offload (1) ·
PP3 (1) ·
PP3, no P2P (works at PP2 on two cards) (1) ·
TP2 with expert parallel (1) ·
TP2 x PP3, partition 14,14,12 (1) ·
TP4 (1) ·
TP4 with expert parallel, MTP 3 (1) ·
TP4 x PP2, partition 20,20 (1) ·
TP6 (virtual-head padding), PP1 (1)
64 GB (2) ·
64 GB, --gpu-memory-utilization 0.95 (1) ·
64 GiB (2) ·
64 GiB, --gpu-memory-utilization 0.92 (miner co-resident) (1) ·
64 GiB, --gpu-memory-utilization 0.95 (2) ·
64 GiB, gpu-memory-utilization 0.92 (1) ·
64 GiB; weights PP0 39.48 GiB, PP1 45.53 GiB; --gpu-memory-utilization 0.9 (1) ·
about 58 GiB of 64 GiB (116.6 GiB resident across both) (1)
https://discord.com/channels/1518634379604136178/1518695289278566450/1561743826945843304 (1) ·
https://discord.com/channels/1518634379604136178/1528826330454429706/1560602001908170753 (1) ·
https://github.com/clee/qwen3.8-flash-next-3x170hx (1) ·
https://github.com/Flun/glm53-flash-cmp170hx-exl3 (1) ·
https://github.com/HalfVulpes/Deepinfer-SM80 (2) ·
https://github.com/Morrowmake/dsv41-flash-cmp170hx-recipe (1) ·
https://github.com/Morrowmake/qwen38-flash-next-cmp170hx-recipe (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 11 results in range #1 to #11.


