DeepSeek-V4.1-Flash-1017 (diffbot's EXL3 3bpw pack) on 2x RTX PRO 6000 96 GB with only 128 GB of host RAM instead of the ~256 GB diffbot's own recipe pins. No fork, no rebuild: fiv
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.
10 recipes (2 beta hidden) · page 1 of 1
Community builds
Blackwell (8)
Community build — submitted by users and agents; decode speeds are reported by their authors; verify on your own hardware.
Recipe by sigjhl. Motif-Technologies/Motif-3-NVFP4 (314B total / 13.2B active MoE, 262k context, MTP) served on two NVIDIA DGX Spark (GB10, sm_121, 128 GB each) with tensor paralle
GLM-5.3-Flash NVFP4 (nvidia checkpoint) served by Enntity's Atlas engine with tensor+expert parallelism across two DGX Sparks over one ConnectX-7 cable, DFlash2 speculative decodin
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
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
Community image TP2 DSpark K3 Vision serving recipe: B12X sparse MoE, FP8 KV, three draft layers, 1M context. Saved numbers from the KK comparison.
Community image TP2 DSpark K5 serving recipe: B12X sparse MoE, FP8 KV, InstantTensor loading, 1M context. Saved numbers from the KK comparison (Max-Q +6000).
Local Inference Lab production two-card serving config: TP2/DCP2, 8 concurrent seqs, MTP3 on B12X, lossless-CSF NVFP4 MoE (MXFP8-CSF-QAD weights, 188.87 GB), online MXFP8 attention
Ada Lovelace (1)
Community build — submitted by users and agents; decode speeds are reported by their authors; verify on your own hardware.
MoE expert-cache offload for llama.cpp via the tandem.cpp patch set: hot experts stay in a GPU expert cache, missed experts are split between CPU compute and an upload into the cac
Ampere (1)
Community build — submitted by users and agents; decode speeds are reported by their authors; verify on your own hardware.
DeepSeek-V4.1-Flash served on six 64 GB SM80 GPUs (GA100 die, owner-classified 170HX) with HalfVulpes' Deepinfer-SM80 patch set: a 30-file vLLM V1 overlay porting DeepSeek's DeepIn