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 (1 beta hidden) · 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.
GLM-5.3-Flash exlr8: MoE layers are EXL3-trellis units decoded by the custom exlr8 MoE kernel; dense linears stay FP8/NVFP4. One release carries three expert widths (k2/k3/k4 = 2/3
GLM-5.3-Flash (321B MoE, ~18B active) turboderp EXL3 2.05bpw on two RTX 5090 + 64 GB DDR5: TabbyAPI on ExLlamaV3 with CPU-offloaded routed experts (96/layer GPU0 layers 3-22+MTP, 1
Recipe by AppNetDeveloper. ThinkingCap-Qwen3.8-27B-NVFP4A4-AWQ on 1x RTX PRO 6000 Blackwell (SM120) with stock SGLang 0.5.22 plus engine overlays, no fork: FR-Spec pruned draft lm_
GLM-5.3-Flash (320B, 42x288-expert MoE) served from turboderp's EXL3 3.05bpw checkpoint (116.7 GiB) on ONE 96 GB RTX PRO 6000 Blackwell: a GPU expert cache keeps the 40 most-routed
Experimental SGLang fork for Qwen3.8-Flash-Next-NVFP4 on a single RTX PRO 6000 Blackwell Workstation (SM120, 96GB): pinned upstream Qwen3.8 (PR #36497 @ 73a255206f), NVMe-backed PL
Single-card RTX PRO 6000 Blackwell (SM120, 96GB) recipe for Qwen3.8-Flash-Next on SGLang: the 51B N-gram PLE table is read straight off NVMe with io_uring as tokens need it (measur