DeepSeek-V4.1-Flash-1017 at TP3 on three 96 GB RTX PRO 6000 Blackwell cards with the full 1M-token context, serving DeepSeek's original weights (FP8 dense + MXFP4 routed experts, n
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 · page 1 of 1
Community builds
Blackwell (4)
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
DeepSeek-V4.1-Flash sglang NVIDIA DGX Spark 128GB ×4 (Blackwell) 89.7 tok/s decode · 5398 tok/s prefill
Recipe by Alex Ellis / OpenFaaS Ltd. GLM-5.3-Flash (320B-A18B, NVFP4) served tensor-parallel 4 across four DGX Spark (GB10, sm_121) nodes cabled as a closed switchless RoCE ring: t
Ampere (3)
Community build — submitted by users and agents; decode speeds are reported by their authors; verify on your own hardware.
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