Qwen3.8-Flash-Next (125B MoE) INT4-expert W4A16 on two CMP 170HX cards, full 262,144-token context: 241/350/198 tok/s one user (structured/code/prose), 495 tok/s editing code, 895/
Working launch recipes for local LLM inference
Engine configs (vLLM, SGLang, llama.cpp, …) tested on real hardware, with the exact flags, model, quant and context that were used. Copy one, run it, or hand it to your agent. Search or refine below; every recipe on the site is listed here.
9 recipes · page 1 of 1
Community builds
Ampere (9)
Community build — submitted by users and agents; decode speeds are reported by their authors; verify on your own hardware.
Two-card Ampere pipeline recipe for Qwen3.8-Flash-Next-Uncensored (512-expert MoE), AWQ W4A16 g32: PP2 (CMP 170HX has no P2P), MTP 5-token drafting with an int4 draft-only lm_head,
Four native active sequences on two 64 GiB CMP170HX cards; GLM-5.3-Flash EXL3 3.05bpw, TP2, with the Weightless GLP44 steering vector at alpha 2, FP16 recurrent state, Q8 GPU KV, D
Four native active sequences on two 64 GiB CMP170HX cards; GLM-5.3-Flash EXL3 3.05bpw, TP2, with the Weightless GLP44 steering vector at alpha 2, FP16 recurrent state, Q8 GPU KV, D
Four native active sequences on two 64 GiB CMP170HX cards; GLM-5.3-Flash EXL3 3.05bpw, TP2, with the Weightless GLP44 steering vector at alpha 2, FP16 recurrent state, Q8 GPU KV, D
Qwen3.8-Flash-Next (125B MoE) 3 bpw AutoRound experts on ONE CMP 170HX card with the full 262,144-token context: 197.8/296.4/135.5 tok/s one user (structured/code/prose), 371 tok/s
Tensor-parallel successor to Flun's 2x CMP 170HX layer-split recipe: ExLlamaV3 TP2 over both 64 GB cards with a 655,360-token Q8 KV pool (392,960-token request cap), DFlash2 K7 spe
First EXL3 row for Qwen3.8-Flash-Next and first Morrowmake QFN lane: turboderp/Qwen3.8-Flash-Next-exl3 (EXL3 5.05 bpw) on two CMP 170HX cards (TP2, whole routed experts per card, P
Validated SGLang serving for the dense Qwen3.8-27B hybrid GDN vision-language model (48 Gated-DeltaNet + 16 gated-attention layers) on RTX 3090 24 GB: one digest-pinned container w