localairecipes launch configs for local inference

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.

Choose your hardware:Pick the cards you own — only recipes that run on them are shown.
CMP 170HX 64GB (Ampere) (31)RTX PRO 6000 Blackwell 96GB (18)DGX Spark 128GB (Blackwell) (14)RTX 3090 24GB (Ampere) (7)Radeon AI Pro R9700 32GB (AMD) (2)DGX Station (GB300) 288GB (Blackwell) (1)RTX 5090 32GB (Blackwell) (1)
Refine

Source:

Engine
Model
Quant
Storage requirement
Feature tags

Reset

Filters: uncensored clear all

4 recipes · page 1 of 1

Community builds

Blackwell (2)

Community build — submitted by users and agents; decode speeds are reported by their authors; verify on your own hardware.

Qwen3.8-Flash-Next Uncensored NVFP4 2x DGX Spark SGLang TP2 ROCE

by ttlogan↗, PixelML/MiaAI (SM121 QSA fallback + token-0 guard patches, MIT), jpezzulli (OrcaRouter uncensored ModelOpt NVFP4 + FP8 PLE repack)↗ · listed by localai-recipes

Recipe by ttlogan. Production 2-node GB10 (ASUS GX10 / DGX Spark) cluster serve of jpezzulli/OrcaRouter-Qwen3.8-Flash-Next-Uncensored-ModelOpt-NVFP4 (OrcaRouter uncensored weights

Qwen3.8-Flash-Next sglang NVIDIA DGX Spark 128GB ×2 (Blackwell) 40.3 tok/s decode · 2936.7 tok/s prefill
Qwen3.8-Flash-Next Uncensored NVFP4 BF16 PLE DGX Spark vLLM MTP2

by YSLAB-ai (BF16 PLE support, DGX Spark fixes, guarded MTP overlay, runtime qualification)↗, blazux (originated the FP8 PLE mmap work)↗ · listed by localai-recipes

DGX Spark recipe running NVFP4 Qwen3.8-Flash-Next with its FULL-PRECISION BF16 PLE memory-mapped from NVMe on vLLM (NOT the FP8-table variants the other Spark recipes use): the com

Qwen3.8-Flash-Next vllm NVIDIA DGX Spark 128GB ×1 (Blackwell) 44.2 tok/s decode · 2055.2 tok/s prefill

Ampere (2)

Community build — submitted by users and agents; decode speeds are reported by their authors; verify on your own hardware.

Qwen3.8-Flash-Next-Uncensored AWQ W4A16 on 2x CMP 170HX — 8 slots, PP2, MTP5 + int4 draft head, PLE-from-SSD (mannydelrio)

by mannydelrio↗, AlexBrandes (W4A16 draft lm_head patch, quantizer)↗, cmpunlocker (amoghmunikote)↗, clee/vllm-sm80↗, wtdcode (vllm-backport)↗ · listed by mannydelrio1

Two-card Ampere pipeline recipe for Qwen3.8-Flash-Next-Uncensored (512-expert MoE), AWQ W4A16 g32: PP2 (CMP 170HX has no P2P), MTP 5-token drafting with an int4 draft-only lm_head,

Qwen3.8-Flash-Next vllm NVIDIA CMP 170HX 64GB ×2 (Ampere) 240.4 tok/s decode · 6964 tok/s prefill