01 · The idea
What stayed with us
These decentralized inference projects make the latency cost of sharding a model across machines tangible. The comparison helps explain why a single-Mac specialist is a different system.
02 · The local translation
What we did with it
Together these projects illustrate two shapes of decentralized inference: sharding work across machines and making complete models available across a network. The distinction sharpens our understanding of per-token latency and what locality buys a small specialist.
03 · The boundary
Where the comparison stops
We have not installed or benchmarked either system for PostTrainLLM. Their networked design is a comparison at the distributed boundary, not a substitute for a verified local decode and task-completion gate.