Show HN: Nibia Fabric – Turn everyday computers into a shared AI compute cluster
I built NIBIA Fabric, an open-source distributed LLM inference system that pools CPU and RAM across macOS, Linux, and Windows machines. It builds on llama.cpp, with capacity-aware scheduling, adaptive memory reservation, persistent tensor caching, and an OpenAI-compatible API. I’ve validated the current alpha on three physical machines across several models, including a 30B-class Qwen model, as well as GPT-OSS 20B. Feedback on the architecture and use cases is very welcome.
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Recorded metrics
points · 1 votes
2026-10-31 12:30:22.362Z
cumulative since publication
Research analysis
Project record
Repository: nibia-ai/fabric. Source account: I built NIBIA Fabric, an open-source distributed LLM inference system that pools CPU and RAM across macOS, Linux, and Windows machines. It builds on llama.cpp, with capacity-aware scheduling, adaptive memory reservation, persistent tensor caching, and an OpenAI-compatible API. I’ve validated the current alpha on three physical machines across several models, including a 30B-class Qwen model, as well as GPT-OSS 20B. Feedback on the architecture and use cases is very welcome.
Observed adoption & comparisons
Source votes: 1 votes (Oct 31, 2026 UTC; cumulative since publication); previous same-scope observation: 1 (Oct 31, 2026 UTC). These observations do not establish demand growth. Recent records to compare: “Show HN: An open source webbluetooth and NFC browser for iOS” (Show HN, Nov 6, 2026); “astral-sh/ruff” (GitHub Trending, Nov 6, 2026). Shared themes are navigation hints; they have not been verified as the same need or independent corroboration.
Adoption decision
Evaluate it against the tool or manual workflow it would replace. The stored record supports discovery; it does not yet establish reliability, lower operating cost or commercial adoption.
Unverified requirements
Not verified: license permissions, required hardware, installation steps, maintenance status and a representative task with its failure cases.
Next test
Choose one real task and a current alternative. Record the commit, environment, input, output, elapsed time and failure behavior; retain the result before recommending adoption.