Community image TP4 serving recipe: MTP3 draft, B12X MoE, FP8 KV, 1M context. Saved five-window medians (Max-Q +6000), the reference the Karmic Kraken report compares against.
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.
7 recipes (2 beta hidden) · page 1 of 1
Community builds
Blackwell (3)
Community build — submitted by users and agents; decode speeds are reported by their authors; verify on your own hardware.
Community image TP4 serving recipe: DFlash2 K7 offline-MXFP8 draft, B12X MoE, FP8 KV, 1M context. Saved five-window medians (Max-Q +6000).
Community image TP4 serving recipe: adaptive DSpark K7, RAM Engram ngram tables, 131K context cap. Saved numbers from the KK comparison.
DeepSeek-V4.1-Flash vllm NVIDIA RTX PRO 6000 Blackwell 96GB ×4 230.6 tok/s decode · 17503 tok/s prefill
Ampere (4)
Community build — submitted by users and agents; decode speeds are reported by their authors; verify on your own hardware.
Four-card RTX 3090 recipe for Qwen3.8-Flash-Next (125B MoE) in halt95's W4A16-Merlin build (Intel AutoRound INT4 + FP8 PLE + MTP draft, @ 17284bf): TP2 x PP2 with expert parallel,
320B MoE served on ONE 24 GB RTX 3090 with stock exllamav3 1.5.1 plus process-local monkeypatches: the 12,096 routed experts (42 MoE layers x 288, 8 picked per token per layer) tie
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
Single-card RTX 3090 recipe for Qwen3.8-Flash-Next (125B MoE) in the albucino W4A16-FP8PLE build (@ ef55414, same checkpoint as the 2x3090 runtime): the GPU holds attention + dense