Drilldown: Recipe

DeepSeek V4 Flash 0731 on 3x 8 GB (PP3 DSpark) (1) · DeepSeek V4.1 Flash on 2x 8 GB (Project Maya) (1) · DeepSeek V4.1 Flash on 6x 8 GB (TP6) (1) · GLM 5.3 Flash on 2x 8 GB (ExLlamaV3 layer split) (1) · GLM 5.3 Flash on 3x 8 GB (1) · GLM 5.3 Flash on 8x 8 GB (promisezackr PP8 NVFP4 DFlash2) (1) · GLM 5.3 on 8x 8 GB (Int4-Int8Mix PP8) (1) · Qwen3.8 27B on 1x 8 GB (W4A16 vLLM) (1) · Qwen3.8 Flash Next on 2x 8 GB (vektorprime PP2) (1)

DeepSeek-V4-Flash-0731 (1) · DeepSeek-V4.1-Flash-1017 (1) · DeepSeek-V4.1-Flash-1017 (748B in the Maya README) (1) · GLM-5.3 (full 78-layer, not Flash) (1) · GLM-5.3-Flash (1) · GLM-5.3-Flash (320B MoE, 18B active) (2) · Qwen3.8-27B (dense) (1) · Qwen3.8-Flash-Next (1)

Native MXFP4 experts + FP8 attention (official deepseek-ai checkpoint, about 155.4 GiB); KV cache fp8 · Official checkpoint (FP8 dense, FP4 experts); software FP8 on sm_80 · AutoRound 3 bpw + MTP: klee100/Qwen3.8-Flash-Next-AutoRound-3bpw-MTP; 95.37 GiB BF16 PLE table on SSD · AutoRound mixed 2/3-bit (klee100, 2.998 effective bpw, 47 GiB on card) plus halt95's FP8 n-gram table in host RAM · AWQ INT4: cyankiwi/Qwen3.8-Flash-Next-AWQ-INT4; 95 GiB BF16 PLE table in host RAM (FP8 table optional) · AWQ W4A16 g32 (leoncca/Qwen3.8-Flash-Next-Uncensored-AWQ-g32); PLE table converted to INT4 and served from SSD · AWQ W4A16: wtdcode/Qwen3.8-Flash-Next-AWQ-W4A16 · EXL3 3.05 bpw: turboderp/GLM-5.3-Flash-exl3 · EXL3 3.05 bpw: turboderp/GLM-5.3-Flash-exl3; DFlash2 drafter converted to EXL3 6 bpw · EXL3 3.5 bpw (LibertAIDAI; routed experts 4 bpw on layers 0-9 and 30-39, 3 bpw on 10-29; attention and shared experts 6 bpw) plus the official Engram shards · EXL3 4 bpw (Mia-AiLab/GLM-5.3-Flash-EXL3-TR3-4bpw, 164 GB) plus an offline-generated Marlin INT4 decode sidecar (151 GB) · EXL3 5.05 bpw; PLE (n-gram) table read from SSD · GLM-5.3-Int4-Int8Mix (INT4 / INT8 mix); KV fp8_ds_mla · GLM-5.3-Int4-Int8Mix-AWQ-g64; drafter incoai/GLM-5.3-DFlash2 · Hybrid: all 40 expert layers at 3 bits (LibertAI 2.9 bpw pack with layers 18-22 grafted from the 3.5 bpw pack), official FP8 dense tensors · Hybrid: LibertAI EXL3 2.9 bpw routed experts (layers 0-39), official FP8/FP4 for attention, shared experts, head, draft and Engram; KV fp8_ds_mla · Hybrid: routed experts EXL3 (mostly 3 bpw, 2 bpw on layers 18-22) from LibertAI's 2.9 bpw pack; attention, shared experts, indexer and DSpark drafter official FP8; KV fp8_ds_mla · Hybrid: routed experts EXL3 2.9 bpw (LibertAIDAI), everything else official FP8/BF16 with the 38 heretic attn.wo_b tensors · Hybrid: routed experts EXL3 2.9 bpw (LibertAIDAI), everything else official FP8/BF16; KV BF16 · LibertAIDAI/GLM-5.3-Flash-NVFP4 (ModelOpt NVFP4, routed experts only, 181 GiB), run through Marlin W4A16 kernels; GLM-5.3-Flash DFlash2 drafter (5 sliding-window layers) · Maya GGUF (Maya-S 340 GB or Maya-XS 277 GB; Engram tables stay on SSD) · Maya-M GGUF (116 GB), every expert in VRAM; Maya-S (96.5 GB) and Maya-S24 (94.7 GB) also reported · Mixed INT4 AutoRound (Minachist/Qwen3.8-Flash-Next-INT4-Mixed-AutoRound, about 65 GB) with the full BF16 PLE table on NVMe · NVFP4 (ModelOpt mixed precision): ProCreations/MiMo-V2.6-Flash-RL-NVFP4, run as 4-bit weights with BF16 activations through Marlin · NVFP4 main weights through Marlin (no native FP4 on sm_80), mixed FP8 and BF16 MTP weights, FP8 PLE table (about 51.2 GB) kept on NVMe · Official block FP8, about 126 GiB including the about 50 GiB FP8 n-gram (PLE) table, which is served from host RAM; KV cache bf16 (the QSA layers refuse fp8) · Official checkpoint (510 GB: MXFP4 routed experts, FP8 dense layers, 203 GB FP8 Engram table); KV cache fp8_ds_mla · Official checkpoint as downloaded: MXFP4 routed experts as Marlin W4A16, MXFP8 dense as Marlin W8A16; KV fp8 · Official checkpoint: dense MXFP8 through an SM80 Marlin W8A16 path, routed experts MXFP4 through W4A16; KV FP8 · W4A16 (INT4 weights, group size 128, FP16 activations): canada-quant/GLM-5.3-Flash-W4A16-MTP · W4A16 (INT4 weights, Marlin kernels) · 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) · W4A16 INT4 g128 with the PLE table repacked INT4 g32: zankich/Qwen3.8-Flash-Next-W4A16-Merlin-INT4PLE-FP8KV; KV BF16 · W4A16, about 4.3 bpw, 163 GiB on disk · W4A16: halt95/Qwen3.8-Flash-Next-W4A16-Merlin (AutoRound INT4 g128 experts, INT8 GDN projections, FP8 n-gram table) · Other · None

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 (via the glm53-flash-cmp170hx-recipe) (1) · Project Maya (Strata and llama.cpp lineage) (1) · vLLM (1) · vLLM (build not stated) (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) · vLLM: official vllm/vllm-openai:glm53-flash image plus promisezackr's 24 patches (SM8x base from 344303947/vllm170hx, pipeline parallel for GLM-5.3-Flash, DFlash2 under PP8) (1)

None (3) · 0.1.dev1+ga5530b90c.d20260829 (1) · f8ea5bb-era base with the allover326 patch set as of mid-August 2026 (1) · Flun/glm53-flash-cmp170hx-exl3 at 5933112 (1) · not stated (1) · reports 0.0.0; patches/public-base-to-runtime.patch (109 files) (1) · v0.1.dev20051+g487ecf187 (the image's vLLM); repo commit 90ec72e (1)

8 GB (9)

1 (1) · 2 (3) · 3 (2) · 6 (1) · 8 (2)

engine-managed expert tiering across VRAM, host RAM and NVMe (1) · layer split (no tensor parallel), no P2P (1) · PP2 with PLE CPU offload (1) · PP3 (1) · PP3 (VLLM_PP_LAYER_PARTITION=15,15,13) with DSpark, 5 draft tokens; PCIe Gen2 x4, no P2P (1) · PP8 (VLLM_PP_LAYER_PARTITION=7,5,7,5,7,5,7,2) with DFlash2, 7 draft tokens; PCIe Gen2 x4, no P2P; host 2x Xeon E5-2690 v4, 96 GB DDR4 (1) · PP8, TP1 (1) · single card (1) · TP6 (virtual-head padding), PP1 (1)

None (1) · 1,048,576 (2) · 1,048,576 (KV pool 6.62M tokens at utilization 0.90, about 6.3 concurrent 1M requests) (1) · 262,144 (1) · 262,144 native (YaRN to about 500k in the recipe's long variant) (1) · 392,960 (profile q8_384k) (1) · 691,968 (largest stable context at --gpu-memory-utilization 0.965) (1) · about 1,000,000 (8-bit KV cache) (1)

None (1) · 20 - 80 (5) · 80 - 170 (3)

None (9)

64 GB (2) · 64 GB (65,536 MiB), --gpu-memory-utilization 0.965 (1) · 64 GB, --gpu-memory-utilization 0.90 (1) · 64 GB, --gpu-memory-utilization 0.95 (1) · 64 GiB (1) · 64 GiB, --gpu-memory-utilization 0.97 (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)

None (7) · 100 W (1) · 200 W (1)

None (8) · 610.43.02 (nvidia-driver-610-open) (1)

None (7) · cmpunlocker 360acd7 (2026-08-06), 8 GB profile (1) · cmpunlocker VRAM unlock (64 GB per card) (1)

Proven (2) · Reported (7)

1a2b3c0967 (1) · 68x0 (1) · pcsv. (1) · promisezackr (1) · shirogeek (1) · tim-odonnell (1) · vektor_prime (1) · wakeupmbrc1 (1) · wgkbfcp (1)

August 2026 (4) · October 2026 (5)

https://github.com/HalfVulpes/Deepinfer-SM80 · https://github.com/Morrowmake/dsv41-flash-cmp170hx-recipe · https://github.com/mw00/project-maya#how-fast-is-it · https://discord.com/channels/1518634379604136178/1518695289278566450/1561743826945843304 · https://discord.com/channels/1518634379604136178/1528826330454429706/1557523316120748073 · https://discord.com/channels/1518634379604136178/1528826330454429706/1560602001908170753 · https://github.com/344303947/dsv41-flash-pp5-170hx · https://github.com/alanoo81/deepseek-v41-flash-4x-cmp170hx · https://github.com/alexvnesta/glm53-flash-cmp170hx-tp · https://github.com/allover326/deepseek-v4-cmp170hx · https://github.com/anoane/09-deepseek-v41-flash-1017-exl3-tp5-1m · https://github.com/arvkmr/vllm-170hx-glm5 · https://github.com/clee/qwen3.8-flash-next-3x170hx · https://github.com/CurtisAccelerate/DeepSeekv41-Flash-1017-EXL3-CMP170HX · https://github.com/DUFRelic/qwen38-flash-next-recipe · https://github.com/ElectroHobo/mimo-v26-flash-cmp170hx-recipe · https://github.com/ElectroHobo/qwen38-flash-next-3bpw-1x-cmp170hx-recipe · https://github.com/ElectroHobo/qwen38-flash-next-w4a16-2x-cmp170hx-recipe · https://github.com/Flun/glm53-flash-cmp170hx-exl3 · https://github.com/GNmac-lab/deepseek-v41-flash-1017-cmp170hx · https://github.com/iIIusi0n/qwen3.8-flash-next-cmp170hx · https://github.com/kaka86mm/glm53-flash-170hx · https://github.com/mannydelrio/qwen38-flash-next-2x-cmp170hx · https://github.com/Morrowmake/glm53-flash-cmp170hx-recipe · https://github.com/Morrowmake/qwen38-flash-next-cmp170hx-recipe · https://github.com/nguyenthimy2022kg-alt/Qwen-Flash-SM80-170HX · https://github.com/promisezackr/glm53-flash-170hx-pp8 · https://github.com/tim-odonnell/cmp170hx-deepseek-v4-flash · https://github.com/vektorprime/qwen38-flash-next-pp2 · https://github.com/wgkbfcp/deepseek-v4.1-flash-1017-cmp170hx-recipe · https://github.com/Zanooda/deepseek-v4.1-flash-cmp170hx · https://github.com/zebgop-ops/qwen38-flashnext-pp · https://www.bozocentral.com/2x-cmp170hx-qwen3-flash-next · https://www.reddit.com/r/LocalLLM/comments/1w2f03e/qwen3827b_w4a16_on_one_64_gb_cmp_170hx_16070/ · Other · None