sparkrun, Spark Arena simplify DGX Spark vLLM

It feels like the Ollama mental model for a much weirder machine: the real win is turning DGX Spark's recurring setup pain into a shared recipe layer. If the registry keeps growing, sparkrun could become the default known-good path for Spark owners.

• –NVIDIA’s own DGX Spark vLLM docs still include troubleshooting for CUDA mismatches, SM_121a patches, and two-node networking, so the need for guardrails is obvious.
• –sparkrun collapses the messy parts into one workflow: cluster setup, recipe selection, model distribution, and detached job management.
• –Spark Arena turns configs into a searchable benchmark registry, which is much more durable than forum snippets or random gists.
• –Multi-runtime support matters because vLLM, SGLang, and llama.cpp cover different model and memory tradeoffs without forcing separate tooling.
• –The upside is huge for DGX Spark owners, but the scope is narrow enough that everyone else can ignore it.