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.

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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)RTX 5090 32GB (Blackwell) (2)DGX Station (GB300) 288GB (Blackwell) (1)DGX Spark 128GB (Blackwell): Any4 cards (3)3 cards (1)2 cards (3)1 card (10)Radeon AI Pro R9700 32GB (AMD): Any2 cards (1)1 card (1)
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4 recipes · page 1 of 1

Community builds

Blackwell (3)

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

DeepSeek-V4.1-Flash on 4x DGX Spark — tuned TP4 SGLang/DSpark

by knapcio (TP4 profile, v2.1-v2.3 production stack, release gates)↗, MiaAI-Lab (upstream: launcher, Engram store, b12x routing, memory model)↗, 0xSero (original 4x RTX PRO 6000 recipe skeleton boot.py derives from)↗, kpham-sgl (sglang#39187 bounded dense-indexer prefill transient, backported as adapter/indexer_chunked*.py)↗, BBuf + dsv4.1 branch contributors (#39370, #39646, #39648, #39653 decode kernel work in the canary images)↗, rhys101 (SG17 RoCEnante overlay + SG18 prefill TP split from DeepSeek-V4.1-Flash-vLLM-DGX-Spark-8)↗, local-inference-lab — Luke Alonso & Jason (b12x: RoCEnante one-shot RDMA all-reduce + fused MoE kernels)↗, FujitsuPolycom + sparkring contributors (switchless-ring opposite-node paths, path-aware RoCEnante)↗, Saolence (#1/#2/#4/#5/#6: ring as opt-in switch, NCCL/build fixes, first-deployer review)↗, rsync/rchmagos (#8: RoCEnante on the switchless ring, DSV41_ROCE_RING + scripts/ring_mesh/)↗ · listed by localai-recipes

DeepSeek-V4.1-Flash sglang NVIDIA DGX Spark 128GB ×4 (Blackwell) 89.7 tok/s decode · 5398 tok/s prefill

AMD (1)

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

Qwen3.8-27B PARO-MXFP6 2x Radeon AI PRO R9700 — vLLM radiance TP2

by vinniefranco (podman compose stack, TLS/egress design, bench harness)↗, hugypufy (PARO MXFP6 checkpoint + radiance-vllm-mxfp4 fork + published perf table)↗, StillDeadcode (vllm-radiance image, radiance engine, libr4d)↗, tcclaviger (DFlash2-FP8 drafter)↗ · listed by localai-recipes

27B Qwen served from a single podman-compose stack on two Radeon AI PRO R9700 (gfx1201) cards: vLLM runs the prebuilt stilldeadcode/vllm-radiance:0.9.3 image (RDNA4 patches + tuned

Qwen3.8-27B vllm AMD Radeon AI Pro R9700 32GB ×2 138 tok/s decode · 3348 tok/s prefill