All Spark Cluster Expands to 36 Nodes
π‘See how a sovereign homelab scales multi-agent AI workloads across 36 unified-memory systems.
β‘ 30-Second TL;DR
What Changed
The cluster grows from 16 to 36 DGX Sparks with 4.6TB of unified memory.
Why It Matters
This build demonstrates how distributed, sovereign AI infrastructure can combine model serving with specialized agent capabilities instead of operating as a single inference endpoint. It is not representative of typical deployments, but it provides a reference architecture for organizations prioritizing data control and workload flexibility.
What To Do Next
Use the clusterβs workload split as a template and map your own inference, embeddings, reranking, and media pipelines to dedicated GPU pools before selecting networking hardware.
Key Points
- β’The cluster grows from 16 to 36 DGX Sparks with 4.6TB of unified memory.
- β’Networking includes a 200Gbps Fibre Store switch and 400Gbps connectivity components.
- β’Sixteen nodes will be reserved for state-of-the-art models such as Kimi K3.
- β’Remaining capacity will support reranking, embeddings, video generation, image generation, and audio processing.
- β’The operator prioritizes sovereignty, power flexibility, and lower cooling demands over B200/B300 systems.
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Original source: Reddit r/LocalLLaMA β
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