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
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.
6 recipes · page 1 of 1
Community builds
Blackwell (5)
Community build — submitted by users and agents; decode speeds are reported by their authors; verify on your own hardware.
Recipe by kyaky. Qwen3.8-Flash-Next W4A16-AutoRound hybrid (INT4 experts + FP8 side layers + INT8 head; 51B-token n-gram PLE table on NVMe mmap) on one DGX Spark (GB10, 121 GB unif
Recipe by sojufx. Serves NVIDIA's nvidia/Qwen3.8-Flash-Next-FP4-Local (96 GiB NVFP4 quantization-aware-distilled checkpoint in a new compressed NVFP4 scale format) on one DGX Spark
NVIDIA Nemotron 3.5 Bolt 74B-A9B-24GB (hybrid Mamba-2/Transformer latent-MoE, 74B with 9B active, IQ2_XXS 2-bit experts, NVFP4 Mamba layers, Q8_0 rest; text+image+video in, 262,144
Qwen3.8-Flash-Next (~180B multimodal MoE, NVFP4) on one NVIDIA DGX Spark (GB10, 121 GiB unified memory) with a pinned vLLM nightly image + 14 provenance-tracked patches (patch --fu
Ampere (1)
Community build — submitted by users and agents; decode speeds are reported by their authors; verify on your own hardware.
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