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.
8 recipes · page 1 of 1
Single-card Blackwell recipe for Qwen3.8-Flash-Next (hybrid GDN/QSA MoE, 48 layers, 512 experts) on one 96 GB RTX PRO 6000: the Community image community image R34 (localinferencel
Community build — submitted by users and agents; decode speeds are reported by their authors; verify on your own hardware.
First dense Qwen3.8-27B recipe on 1x DGX Spark (GB10, 128 GB unified): SGLang v0.5.22 release image + NVFP4 target + DFlash2 speculative decoding (NVFP4 calibrated 5-layer draft, d
Recipe by bill-h-lin. Qwen3.8-27B BF16 (BF16 KV, temperature 0) on one RTX PRO 6000 Blackwell Max-Q (SM120) with SGLang made bit-exact under speculative decoding: verify GDN recurr
Recipe by JLFN (dgx-recipe, verified on-box 2026-10-17). Baekpica/GLM-5.3-Flash-VQ-GGUF (72 GiB GVQ1 release directory: routed experts vector-quantized with codebook, 316 nonexpert
Community build — submitted by users and agents; decode speeds are reported by their authors; verify on your own hardware.
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
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
Qwen3.8-27B at 65-67 tok/s decode (74 structured) with MTP k=3 speculative decoding AND prefix caching simultaneously on a single NVIDIA CMP 170HX 64GB (Hynix HBM2e, GA100/sm80), 2
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