blog: iroh for distributed LLM inference (Mesh LLM)#499
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Add a post on how Mesh LLM pools GPUs across machines into a single OpenAI-compatible API, using iroh for peer-to-peer connectivity instead of a central coordinating server.
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Keep the same section structure and wording as the source draft instead of reframing it with an invented narrative hook.
- Rename the route (with a permanent redirect from the old path) and update all internal links (nav dropdown, /solutions listing, homepage card and testimonial link, enterprise page, UseCases component) - Broaden the page beyond a single-company case study: add a Mesh LLM section covering its three request strategies (local execution, peer routing, pipeline splitting), with a pulled quote on retaining control over models, data, and hardware - Keep the existing Nous training content (stats, Psyche, relays, managed monitoring) as its own subsection
These were swept in by an accidental git add -A. Untrack them and fix .gitignore so plain .env (not just .env.local) is excluded.
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Summary
/blog/mesh-llmon how Mesh LLM pools GPUs across machines into a single OpenAI-compatible API, with no central coordinating server, using iroh for peer-to-peer connectivity (NAT traversal, hole-punching, relay fallback)Test plan
/blog/mesh-llm/blogindex, linking to the right slug