From 1x3090 to 20 DGX Sparks: my house fuses were the first bottleneck

​ From the first LLaMA 33B I knew I wanted that magic-like intelligence locally, mine, so nobody could take it away when I needed it. I bought a 3090 for my home PC. Then LLaMA 65B appeared and I was dazzled, it looked like it had all the knowledge in the world. I made two copies, one local and one on my Synology NAS RAID, so I'd never lose it, and bought a second 3090 to run it. I was happy for a year with small coding tasks on LLaMA and Qwen models. Then DeepSeek 671B MoE appeared. Wow, frontier level at home. I upgraded to a Threadripper with 512GB DDR4 and ran it at 8 t/s with experts offloaded to RAM, or Qwen 235B at 10-12 t/s when I wanted speed. I used these for real coding at my job, in OpenWebUI. Then agentic coding took off and this was too slow. At 100k context generation speed halved and prefill made it a beautiful yet agonising experience. So: 16x3090 across P620-based nodes on a 100Gbit network. It ran MiniMax M2, Qwen 235B and even Qwen 397B, as good as anyone could desire. I built an entire paid project with 397B in OpenCode. But bigger models were out of reach, and the house circuit said no: the fuses blew whenever the rig and the electric oven ran together. Heat and stability were issues too. Next came 4x ASUS GB10, after I read they can be linked (3 was the biggest supported config). 397B at 30 t/s on 400W, versus 50-60 t/s at 6kW, rock solid and almost silent. A dream come true. I built two more projects with it. Then MiMo 2.5 Pro and Kimi 2.6 appeared, smarter and more productive. I found no published solution for an 8-node cluster, but I still bought four more GB10s and made it work. 397B ran at FP8 instead of INT4, and 20% faster. I posted the first MiMo 2.5 Pro and Kimi 2.6 solutions on 8xSparks on the NVIDIA forum. I liked the result so much that I talked my older brother into buying his own 8x GB10, so he could run the best open models locally too, in privacy, without depending on API availability and rising costs. His house is a 5-minute walk from mine. When Kimi K3 (2.8T) appeared, biggest and smartes open weights model, we joined the clusters: two 8x clusters for daily use, or one 16x when we want the biggest model at home. After some work I published the first working solution for Kimi K3 on 16x Sparks on the NVIDIA forum. Through multiple iterations, it went from an unusable 7 t/s at 100k context to a fairly usable 20 t/s at 300k. Now we're adding 4 more Sparks, so a smaller, faster model (GLM 5.3 Flash) runs 24/7 while the big cluster runs either GLM 5.3 on 8x plus MiMo 2.6 Pro on the other 8x, or 16x Kimi K3, or Qwen 3.8 2.4T. I'm always tuning speed on the big models and rebuilding vLLM/SGLang images, so always-on smaller cluster made sense, why? Because for all my work projects and my vllm/sglang personal projects, I chose to use only local hosted models, I never paid a comercial model subscription, not because of the cost, but, because of my strong confidence in local models future. They arrive October 2, along with 4 more Sparks for my younger brother, who got caught by the same local AI microbe :)